
# Library to suppress warnings or deprecation notes
import warnings

warnings.filterwarnings("ignore")

# Libraries to help with reading and manipulating data

import pandas as pd
import numpy as np

# Library to split data
from sklearn.model_selection import train_test_split

# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

# To build model for prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.linear_model import LogisticRegression

# Libraries to build decision tree classifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

# To tune different models
from sklearn.model_selection import GridSearchCV

# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    roc_auc_score,
    plot_confusion_matrix,
    precision_recall_curve,
    roc_curve,
)


pd.set_option('display.max_columns', None)

pd.set_option('display.max_rows', 200)


# loading the dataset
data = pd.read_csv("StarHotelsGroup.csv")


# Make a copy of the dataset
df = data.copy()


np.random.seed(1)
data.sample(200)


data.head(10)


data.tail(10)


# check number of rows and columns
data.shape

(56926, 18)


#check for missing data
data.isna().sum().sort_values(ascending=False)

no_of_adults                            0
no_of_children                          0
no_of_special_requests                  0
avg_price_per_room                      0
no_of_previous_bookings_not_canceled    0
no_of_previous_cancellations            0
repeated_guest                          0
market_segment_type                     0
arrival_date                            0
arrival_month                           0
arrival_year                            0
lead_time                               0
room_type_reserved                      0
required_car_parking_space              0
type_of_meal_plan                       0
no_of_week_nights                       0
no_of_weekend_nights                    0
booking_status                          0
dtype: int64


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 56926 entries, 0 to 56925
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          56926 non-null  int64  
 1   no_of_children                        56926 non-null  int64  
 2   no_of_weekend_nights                  56926 non-null  int64  
 3   no_of_week_nights                     56926 non-null  int64  
 4   type_of_meal_plan                     56926 non-null  object 
 5   required_car_parking_space            56926 non-null  int64  
 6   room_type_reserved                    56926 non-null  object 
 7   lead_time                             56926 non-null  int64  
 8   arrival_year                          56926 non-null  int64  
 9   arrival_month                         56926 non-null  int64  
 10  arrival_date                          56926 non-null  int64  
 11  market_segment_type                   56926 non-null  object 
 12  repeated_guest                        56926 non-null  int64  
 13  no_of_previous_cancellations          56926 non-null  int64  
 14  no_of_previous_bookings_not_canceled  56926 non-null  int64  
 15  avg_price_per_room                    56926 non-null  float64
 16  no_of_special_requests                56926 non-null  int64  
 17  booking_status                        56926 non-null  object 
dtypes: float64(1), int64(13), object(4)
memory usage: 7.8+ MB


### Let's check the duplicate data. And if any, we should remove it.


data[data.duplicated()].count()

no_of_adults                            14350
no_of_children                          14350
no_of_weekend_nights                    14350
no_of_week_nights                       14350
type_of_meal_plan                       14350
required_car_parking_space              14350
room_type_reserved                      14350
lead_time                               14350
arrival_year                            14350
arrival_month                           14350
arrival_date                            14350
market_segment_type                     14350
repeated_guest                          14350
no_of_previous_cancellations            14350
no_of_previous_bookings_not_canceled    14350
avg_price_per_room                      14350
no_of_special_requests                  14350
booking_status                          14350
dtype: int64


data.drop_duplicates(inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 42576 entries, 0 to 56924
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          42576 non-null  int64  
 1   no_of_children                        42576 non-null  int64  
 2   no_of_weekend_nights                  42576 non-null  int64  
 3   no_of_week_nights                     42576 non-null  int64  
 4   type_of_meal_plan                     42576 non-null  object 
 5   required_car_parking_space            42576 non-null  int64  
 6   room_type_reserved                    42576 non-null  object 
 7   lead_time                             42576 non-null  int64  
 8   arrival_year                          42576 non-null  int64  
 9   arrival_month                         42576 non-null  int64  
 10  arrival_date                          42576 non-null  int64  
 11  market_segment_type                   42576 non-null  object 
 12  repeated_guest                        42576 non-null  int64  
 13  no_of_previous_cancellations          42576 non-null  int64  
 14  no_of_previous_bookings_not_canceled  42576 non-null  int64  
 15  avg_price_per_room                    42576 non-null  float64
 16  no_of_special_requests                42576 non-null  int64  
 17  booking_status                        42576 non-null  object 
dtypes: float64(1), int64(13), object(4)
memory usage: 6.2+ MB


# Let's look at the statistical summary of the data
data.describe(exclude = "object").T


data.describe(include=["object"])


# get the column names that are object type
object_columns = list(data.select_dtypes(include=['object']).columns)
object_columns

['type_of_meal_plan',
 'room_type_reserved',
 'market_segment_type',
 'booking_status']


for col in object_columns:
    print(data[col].value_counts())
    print("*" * 50)

Meal Plan 1     31863
Not Selected     8716
Meal Plan 2      1989
Meal Plan 3         8
Name: type_of_meal_plan, dtype: int64
**************************************************
Room_Type 1    29730
Room_Type 4     9369
Room_Type 6     1540
Room_Type 5      906
Room_Type 2      718
Room_Type 7      307
Room_Type 3        6
Name: room_type_reserved, dtype: int64
**************************************************
Online           34169
Offline           5777
Corporate         1939
Complementary      496
Aviation           195
Name: market_segment_type, dtype: int64
**************************************************
Not_Canceled    28089
Canceled        14487
Name: booking_status, dtype: int64
**************************************************


for col in object_columns:
    data[col] = data[col].astype('category')


data.describe(include=['category']).T


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 42576 entries, 0 to 56924
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype   
---  ------                                --------------  -----   
 0   no_of_adults                          42576 non-null  int64   
 1   no_of_children                        42576 non-null  int64   
 2   no_of_weekend_nights                  42576 non-null  int64   
 3   no_of_week_nights                     42576 non-null  int64   
 4   type_of_meal_plan                     42576 non-null  category
 5   required_car_parking_space            42576 non-null  int64   
 6   room_type_reserved                    42576 non-null  category
 7   lead_time                             42576 non-null  int64   
 8   arrival_year                          42576 non-null  int64   
 9   arrival_month                         42576 non-null  int64   
 10  arrival_date                          42576 non-null  int64   
 11  market_segment_type                   42576 non-null  category
 12  repeated_guest                        42576 non-null  int64   
 13  no_of_previous_cancellations          42576 non-null  int64   
 14  no_of_previous_bookings_not_canceled  42576 non-null  int64   
 15  avg_price_per_room                    42576 non-null  float64 
 16  no_of_special_requests                42576 non-null  int64   
 17  booking_status                        42576 non-null  category
dtypes: category(4), float64(1), int64(13)
memory usage: 5.0 MB


# function to plot a boxplot and a histogram along the same scale.

# import the library for labelling
import matplotlib.patheffects as path_effects

# import the library for labelling
import matplotlib.patheffects as path_effects

def add_median_labels(ax):
    lines = ax.get_lines()
    # determine number of lines per box (this varies with/without fliers)
    boxes = [c for c in ax.get_children() if type(c).__name__ == 'PathPatch']
    lines_per_box = int(len(lines) / len(boxes))
    # iterate over median lines
    for median in lines[4:len(lines):lines_per_box]:
        # display median value at center of median line
        x, y = (data.mean() for data in median.get_data())
        # choose value depending on horizontal or vertical plot orientation
        value = x if (median.get_xdata()[1]-median.get_xdata()[0]) == 0 else y
        text = ax.text(x, y, f'{value:.1f}', ha='center', va='center',
                       fontweight='bold', color='white', bbox=dict(facecolor='black'),size=15)
        # create median-colored border around white text for contrast
        text.set_path_effects([
            path_effects.Stroke(linewidth=3, foreground=median.get_color()),
            path_effects.Normal(),
        ])
            
def box_and_histogram(column,  figsize=(10,10), bins = None):
    """ Boxplot and histogram together, with median labels on boxplot
    df_series: dataframe column
    figsize: size of fig (default (9,8))
    bins: number of bins (default None / auto)
    color of mean is green and median is black
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(nrows = 2, # Number of rows of the subplot grid= 2
                                           sharex = True, # x-axis will be shared among all subplots
                                           gridspec_kw = {"height_ratios": (.25, .75)}, 
                                           figsize = figsize 
                                            ) # creating the 2 subplots
    box_plot = sns.boxplot(column, ax=ax_box2,showmeans=True, color='red')
    add_median_labels(box_plot.axes)
 

    sns.distplot(column, kde=F, bins=bins) if bins else sns.distplot(column, kde=True, ) # For histogram
    ax_hist2.axvline(np.mean(column), color='g', linestyle='--') # Add mean to the histogram
    ax_hist2.axvline(np.median(column), color='black', linestyle='-') # Add median to the histogram

    
# function to create labeled barplots


def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="RdYlBu",
        order=data[feature].value_counts().index
    
       
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


col = 'arrival_month'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for arrival_month


col = 'arrival_year'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for arrival_year


col = 'arrival_date'
print(' Plot for {}'.format(col))
plt.figure(figsize=(15, 8))
sns.countplot(y=data[col], order = data[col].value_counts().index);

 Plot for arrival_date


col = 'type_of_meal_plan'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for type_of_meal_plan


col = 'room_type_reserved'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for room_type_reserved


col = 'market_segment_type'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for market_segment_type


col = 'booking_status'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for booking_status


cat_columns = object_columns.copy()
cat_columns.append('required_car_parking_space')
cat_columns.append('repeated_guest')
print(cat_columns)

['type_of_meal_plan', 'room_type_reserved', 'market_segment_type', 'booking_status', 'required_car_parking_space', 'repeated_guest']


col = 'required_car_parking_space'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for required_car_parking_space


col = 'repeated_guest'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for repeated_guest


cols =['no_of_adults', 'no_of_children']
for col in cols:
    print(' Plot for {}'.format(col))
    labeled_barplot(data, col, True)

 Plot for no_of_adults

 Plot for no_of_children


cols =['no_of_weekend_nights', 'no_of_week_nights']
for col in cols:
    print(' Plot for {}'.format(col))
    labeled_barplot(data, col, True)

 Plot for no_of_weekend_nights

 Plot for no_of_week_nights


# Let's visualize the data for ['no_of_adults']
columns =  ['no_of_adults']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['no_of_children']
columns =  ['no_of_children']
for col in columns:
    box_and_histogram(data[col])


# Let's visualize the data for ['no_of_weekend_nights']
box_and_histogram(data['no_of_weekend_nights'])


# Let's visualize the data for ['no_of_week_nights']
box_and_histogram(data['no_of_week_nights'])


# Let's visualize the data for ['lead_time']
box_and_histogram(data['lead_time'])


data['no_of_previous_cancellations'].value_counts()

0     42132
1       249
2        66
3        47
11       25
4        24
5        16
6        16
13        1
Name: no_of_previous_cancellations, dtype: int64


# Let's visualize the data for ['no_of_previous_bookings_not_canceled']
box_and_histogram(data['no_of_previous_bookings_not_canceled'])


# Let's visualize the data for ['avg_price_per_room']
box_and_histogram(data['avg_price_per_room'])


# Let's visualize the data for ['no_of_special_requests']
box_and_histogram(data['no_of_special_requests'])


data['no_of_special_requests'].value_counts()

0    19228
1    15571
2     6381
3     1230
4      150
5       16
Name: no_of_special_requests, dtype: int64


plt.figure(figsize=(15, 7))
sns.heatmap(data.corr(), annot=True, vmin=-1, vmax=1, cmap="Spectral")
plt.show()


numeric_cols = list(data.select_dtypes(exclude=['object']).columns)

sns.pairplot(data[numeric_cols], hue="booking_status")
plt.show()


# function to plot stacked bar chart


def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    colors = {'Canceled':'red', 'Not_Canceled':'green'}
    
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 10),  color=colors)
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()
    
    
# Horizontal
def stacked_barplot_h(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    colors = {'Canceled':'red', 'Not_Canceled':'green'}
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    
    tab.plot(kind="barh", stacked=True, figsize=(10 , 10),  color=colors)
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


plt.style.use('ggplot')


stacked_barplot_h(data, 'no_of_week_nights','booking_status')

booking_status     Canceled  Not_Canceled    All
no_of_week_nights                               
All                   14487         28089  42576
2                      3979          7785  11764
3                      3483          6177   9660
1                      3038          7868  10906
4                      1704          2432   4136
5                      1104          1401   2505
0                       691          2106   2797
6                       161           140    301
7                        90            75    165
10                       79            15     94
8                        74            47    121
9                        29            19     48
11                       17             3     20
12                       11             5     16
15                        8             6     14
13                        7             2      9
14                        5             5     10
16                        5             2      7
17                        2             1      3
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'no_of_adults','booking_status')

booking_status  Canceled  Not_Canceled    All
no_of_adults                                 
All                14487         28089  42576
2                  10998         20071  31069
3                   1813          2218   4031
1                   1589          5675   7264
0                     76           108    184
4                     11            17     28
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'no_of_children','booking_status')

booking_status  Canceled  Not_Canceled    All
no_of_children                               
All                14487         28089  42576
0                  12580         25720  38300
1                   1009          1552   2561
2                    883           790   1673
3                     14            25     39
9                      1             1      2
10                     0             1      1
------------------------------------------------------------------------------------------------------------------------


stacked_barplot_h(data, 'no_of_weekend_nights','booking_status')

booking_status        Canceled  Not_Canceled    All
no_of_weekend_nights                               
All                      14487         28089  42576
0                         5630         12199  17829
2                         4417          7570  11987
1                         4130          8130  12260
4                          148            68    216
3                          117           103    220
5                           21             9     30
6                           21            10     31
8                            2             0      2
7                            1             0      1
------------------------------------------------------------------------------------------------------------------------


stacked_barplot_h(data, 'no_of_week_nights','booking_status')

booking_status     Canceled  Not_Canceled    All
no_of_week_nights                               
All                   14487         28089  42576
2                      3979          7785  11764
3                      3483          6177   9660
1                      3038          7868  10906
4                      1704          2432   4136
5                      1104          1401   2505
0                       691          2106   2797
6                       161           140    301
7                        90            75    165
10                       79            15     94
8                        74            47    121
9                        29            19     48
11                       17             3     20
12                       11             5     16
15                        8             6     14
13                        7             2      9
14                        5             5     10
16                        5             2      7
17                        2             1      3
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'type_of_meal_plan','booking_status')

booking_status     Canceled  Not_Canceled    All
type_of_meal_plan                               
All                   14487         28089  42576
Meal Plan 1           10511         21352  31863
Not Selected           3118          5598   8716
Meal Plan 2             857          1132   1989
Meal Plan 3               1             7      8
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'market_segment_type','booking_status')

booking_status       Canceled  Not_Canceled    All
market_segment_type                               
All                     14487         28089  42576
Online                  13483         20686  34169
Offline                   804          4973   5777
Corporate                 167          1772   1939
Aviation                   33           162    195
Complementary               0           496    496
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'repeated_guest','booking_status')

booking_status  Canceled  Not_Canceled    All
repeated_guest                               
All                14487         28089  42576
0                  14477         26784  41261
1                     10          1305   1315
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'no_of_special_requests','booking_status')

booking_status          Canceled  Not_Canceled    All
no_of_special_requests                               
All                        14487         28089  42576
0                           8752         10476  19228
1                           4346         11225  15571
2                           1389          4992   6381
3                              0          1230   1230
4                              0           150    150
5                              0            16     16
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'arrival_year','booking_status')

booking_status  Canceled  Not_Canceled    All
arrival_year                                 
All                14487         28089  42576
2019                7045          9531  16576
2018                6966         15141  22107
2017                 476          3417   3893
------------------------------------------------------------------------------------------------------------------------


stacked_barplot_h(data, 'arrival_date','booking_status')

booking_status  Canceled  Not_Canceled    All
arrival_date                                 
All                14487         28089  42576
17                   544           947   1491
26                   530           939   1469
15                   510           909   1419
27                   507           959   1466
16                   505           922   1427
29                   501           940   1441
8                    496           908   1404
3                    494           918   1412
7                    491           921   1412
12                   489           940   1429
25                   486           818   1304
28                   483           918   1401
2                    480          1033   1513
6                    479           918   1397
11                   474           996   1470
21                   472           927   1399
20                   470           995   1465
4                    467           928   1395
10                   463           909   1372
22                   463           799   1262
24                   463           789   1252
13                   462           961   1423
9                    457           942   1399
18                   456           918   1374
1                    456           861   1317
5                    438           995   1433
23                   433           849   1282
30                   431           810   1241
19                   426          1028   1454
14                   414           839   1253
31                   247           553    800
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'arrival_month','booking_status')

booking_status  Canceled  Not_Canceled    All
arrival_month                                
All                14487         28089  42576
8                   2475          2837   5312
7                   2240          2485   4725
5                   1674          2674   4348
4                   1627          2600   4227
6                   1584          2489   4073
3                   1195          2849   4044
10                   918          2291   3209
9                    888          2169   3057
2                    796          2093   2889
11                   496          1696   2192
12                   340          2045   2385
1                    254          1861   2115
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'no_of_previous_cancellations','booking_status')

booking_status                Canceled  Not_Canceled    All
no_of_previous_cancellations                               
All                              14487         28089  42576
0                                14477         27655  42132
1                                    8           241    249
3                                    1            46     47
13                                   1             0      1
2                                    0            66     66
4                                    0            24     24
5                                    0            16     16
6                                    0            16     16
11                                   0            25     25
------------------------------------------------------------------------------------------------------------------------


stacked_barplot_h(data, 'no_of_previous_bookings_not_canceled','booking_status')

booking_status                        Canceled  Not_Canceled    All
no_of_previous_bookings_not_canceled                               
All                                      14487         28089  42576
0                                        14482         26832  41314
12                                           1            24     25
4                                            1            88     89
6                                            1            51     52
1                                            1           344    345
10                                           1            31     32
49                                           0             1      1
55                                           0             1      1
54                                           0             1      1
53                                           0             1      1
52                                           0             1      1
51                                           0             1      1
50                                           0             1      1
2                                            0           163    163
48                                           0             2      2
47                                           0             1      1
57                                           0             1      1
46                                           0             1      1
45                                           0             1      1
44                                           0             2      2
43                                           0             1      1
56                                           0             1      1
59                                           0             1      1
58                                           0             2      2
41                                           0             1      1
60                                           0             1      1
61                                           0             1      1
62                                           0             1      1
63                                           0             1      1
64                                           0             1      1
65                                           0             1      1
66                                           0             1      1
67                                           0             1      1
68                                           0             1      1
69                                           0             1      1
70                                           0             1      1
71                                           0             1      1
72                                           0             1      1
42                                           0             1      1
38                                           0             1      1
40                                           0             1      1
22                                           0             7      7
5                                            0            85     85
7                                            0            46     46
8                                            0            34     34
9                                            0            32     32
11                                           0            27     27
13                                           0            19     19
14                                           0            20     20
15                                           0            16     16
16                                           0            15     15
17                                           0            12     12
18                                           0            10     10
19                                           0            11     11
20                                           0             9      9
21                                           0             9      9
23                                           0             5      5
39                                           0             1      1
24                                           0             6      6
25                                           0             6      6
26                                           0             6      6
27                                           0             6      6
28                                           0             5      5
29                                           0             4      4
30                                           0             3      3
31                                           0             2      2
32                                           0             2      2
33                                           0             1      1
34                                           0             1      1
35                                           0             1      1
36                                           0             1      1
3                                            0           116    116
37                                           0             1      1
------------------------------------------------------------------------------------------------------------------------


plt.figure(figsize=(8,8))
sns.boxplot(x = 'booking_status', y = 'no_of_previous_bookings_not_canceled', showmeans = True, data = data)
plt.show()


plt.figure(figsize=(8,8))
sns.boxplot(x = 'booking_status', y = 'lead_time', showmeans = True, data = data)
plt.show()


plt.figure(figsize=(8,8))
sns.boxplot(x = 'booking_status', y = 'avg_price_per_room', showmeans = True, data = data)
plt.show()


plt.figure(figsize=(10,10))
sns.boxplot(
        data["avg_price_per_room"],
        data['room_type_reserved'],
        hue=data["booking_status"],
        palette="RdYlGn")

<AxesSubplot:xlabel='avg_price_per_room', ylabel='room_type_reserved'>


plt.figure(figsize=(10,10))
sns.boxplot(
        data["avg_price_per_room"],
        data['type_of_meal_plan'],
        hue=data["booking_status"],
        palette="RdYlGn")

<AxesSubplot:xlabel='avg_price_per_room', ylabel='type_of_meal_plan'>


plt.figure(figsize=(8,8))
sns.boxplot(
        data["market_segment_type"],
        data['avg_price_per_room'],
       
        palette="PuBu")

<AxesSubplot:xlabel='market_segment_type', ylabel='avg_price_per_room'>


# Let us plot the corelation of numeric columns
numeric_columns = data.select_dtypes(exclude = ['category']).columns
numeric_columns

Index(['no_of_adults', 'no_of_children', 'no_of_weekend_nights',
       'no_of_week_nights', 'required_car_parking_space', 'lead_time',
       'arrival_year', 'arrival_month', 'arrival_date', 'repeated_guest',
       'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',
       'avg_price_per_room', 'no_of_special_requests'],
      dtype='object')


# let's plot the boxplots of all columns to check for outliers
numeric_columns_for_outlier= ['no_of_adults', 'no_of_children', 'no_of_weekend_nights',
                              'no_of_week_nights', 'required_car_parking_space', 'lead_time','repeated_guest',
                              'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',
                              'avg_price_per_room', 'no_of_special_requests']
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numeric_columns_for_outlier):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


cols_for_outlier_treatment = ['lead_time' ]
for col in cols_for_outlier_treatment:
    box_and_histogram(data[col])
    plt.title('No transformation applied' + str(col) )
        
    box_and_histogram(np.log(data[col] + 1))
    plt.title('log(' + str(col) +' + 1)')

    box_and_histogram(np.arcsinh(data[col]))
    plt.title('arcsinh(' + str(col) +')')

    box_and_histogram(np.sqrt(data[col]))
    plt.title('sqrt('+ str(col) + ')')


data['lead_time_log_plus_1'] = np.log(data['lead_time'] + 1)


cols_for_outlier_treatment = ['avg_price_per_room' ]
for col in cols_for_outlier_treatment:
    box_and_histogram(data[col])
    plt.title('No transformation applied' + str(col) )
        
    box_and_histogram(np.log(data[col] + 1))
    plt.title('log(' + str(col) +' + 1)')

    box_and_histogram(np.arcsinh(data[col]))
    plt.title('arcsinh(' + str(col) +')')

    box_and_histogram(np.sqrt(data[col]))
    plt.title('sqrt('+ str(col) + ')')


# add custom labels
data['avg_price_bin'] = pd.cut(
    data['avg_price_per_room'], [-np.inf, 50, 100, 200, 250, 300, np.inf],\
    labels = ["Under_50", "50-100", "100-200", "200-250", "250-300", "Above_300"])


# function to check if the booking is only for a weekend
def weekend_only_func(week_end, week_day):
    weekend_only = 0
    if week_end > 0 and week_day == 0:
        weekend_only = 1
    return  weekend_only


data['weekend_only_booking'] = \
    data.apply(lambda x: weekend_only_func(x['no_of_weekend_nights'], x['no_of_week_nights']) ,axis=1)


# function to check if the booking only has adults
def adults_only_func(adult, child):
    adults_only = 0
    if adult > 0 and child == 0:
        adults_only = 1
    return  adults_only


data['adults_only_booking'] = data.apply(lambda x: adults_only_func(x['no_of_adults'], x['no_of_children']) ,axis=1)


plt.figure(figsize=(15,15))
sns.catplot(x='market_segment_type',
             col='adults_only_booking', 
             data=data,
             hue='booking_status',

             kind="count",
             palette ='RdBu');

<Figure size 1080x1080 with 0 Axes>


plt.figure(figsize=(15,15))
sns.catplot(x='market_segment_type',
             col='weekend_only_booking', 
             data=data,
             hue='booking_status',
             kind="count",
             palette ='RdBu');

<Figure size 1080x1080 with 0 Axes>


stacked_barplot(data, 'adults_only_booking','booking_status')

booking_status       Canceled  Not_Canceled    All
adults_only_booking                               
All                     14487         28089  42576
1                       12580         25720  38300
0                        1907          2369   4276
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'weekend_only_booking','booking_status')

booking_status        Canceled  Not_Canceled    All
weekend_only_booking                               
All                      14487         28089  42576
0                        13798         26080  39878
1                          689          2009   2698
------------------------------------------------------------------------------------------------------------------------


import calendar
month_in_chars = df['arrival_month'].apply(lambda x: calendar.month_abbr[x])


data['month_in_chars'] = month_in_chars


data['month_in_chars'] = data['month_in_chars'].astype('category')


full_date = pd.to_datetime(data.arrival_year*10000+data.arrival_month*100+data.arrival_date,format='%Y%m%d', errors='coerce')


data['full_date'] = full_date


# convert date to day of the week
import datetime
full_date_weekday = full_date.dt.day_name()


data['day_of_week'] = full_date_weekday


stacked_barplot(data, 'day_of_week','booking_status')

booking_status  Canceled  Not_Canceled    All
day_of_week                                  
All                14480         28061  42541
Sunday              2456          4162   6618
Saturday            2240          4029   6269
Wednesday           2205          4560   6765
Monday              2174          3713   5887
Tuesday             1900          4153   6053
Friday              1793          3661   5454
Thursday            1712          3783   5495
------------------------------------------------------------------------------------------------------------------------


data['total_no_of_person'] = data['no_of_children'] + data['no_of_adults']


box_and_histogram(data['total_no_of_person'])


stacked_barplot_h(data, 'total_no_of_person','booking_status')

booking_status      Canceled  Not_Canceled    All
total_no_of_person                               
All                    14487         28089  42576
2                       9404         18206  27610
3                       2752          3646   6398
1                       1507          5524   7031
4                        803           686   1489
5                         20            25     45
11                         1             0      1
10                         0             1      1
12                         0             1      1
------------------------------------------------------------------------------------------------------------------------


data['total_no_of_days'] = data['no_of_weekend_nights'] + data['no_of_week_nights']


box_and_histogram(data['total_no_of_days'])


stacked_barplot_h(data, 'total_no_of_days','booking_status')

booking_status    Canceled  Not_Canceled    All
total_no_of_days                               
All                  14487         28089  42576
3                     3987          7081  11068
4                     2989          5141   8130
2                     2649          5468   8117
1                     1711          5906   7617
5                     1285          2325   3610
7                      731           881   1612
6                      594           819   1413
8                      157           140    297
10                      94            66    160
9                       84            80    164
14                      58            11     69
11                      35            25     60
12                      30            16     46
15                      20             5     25
13                      18             7     25
16                       9             3     12
19                       8             3     11
20                       7             4     11
17                       5             2      7
21                       4             4      8
18                       3             2      5
22                       3             2      5
24                       3             0      3
0                        2            97     99
23                       1             1      2
------------------------------------------------------------------------------------------------------------------------


data['day_of_week'] = data['day_of_week'].astype('category')


res = data.groupby(['arrival_year','month_in_chars','day_of_week','booking_status'])['arrival_year','month_in_chars','day_of_week','booking_status'].count()
res.head(20)


plt.figure(figsize=(15,15))
sns.catplot(x='arrival_year',
             col='month_in_chars', 
             data=data,
             hue='booking_status',
             col_wrap=4,
             kind="count",
             palette ='RdBu');

<Figure size 1080x1080 with 0 Axes>


print (data['full_date'].min())
print (data['full_date'].max())

2017-07-01 00:00:00
2019-08-31 00:00:00


col = 'arrival_month'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for arrival_month


res = data.groupby(['arrival_month','booking_status'])['arrival_month'].count().sort_values()
res.head(30)

arrival_month  booking_status
1              Canceled           254
12             Canceled           340
11             Canceled           496
2              Canceled           796
9              Canceled           888
10             Canceled           918
3              Canceled          1195
6              Canceled          1584
4              Canceled          1627
5              Canceled          1674
11             Not_Canceled      1696
1              Not_Canceled      1861
12             Not_Canceled      2045
2              Not_Canceled      2093
9              Not_Canceled      2169
7              Canceled          2240
10             Not_Canceled      2291
8              Canceled          2475
7              Not_Canceled      2485
6              Not_Canceled      2489
4              Not_Canceled      2600
5              Not_Canceled      2674
8              Not_Canceled      2837
3              Not_Canceled      2849
Name: arrival_month, dtype: int64


col = 'market_segment_type'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for market_segment_type


plt.figure(figsize=(8,8))
sns.boxplot(
        data["market_segment_type"],
        data['avg_price_per_room'],
       
        palette="PuBu")

<AxesSubplot:xlabel='market_segment_type', ylabel='avg_price_per_room'>


col = 'booking_status'
print(' Plot for {}'.format(col))
labeled_barplot(data, col, True)

 Plot for booking_status


stacked_barplot(data, 'repeated_guest','booking_status')

booking_status  Canceled  Not_Canceled    All
repeated_guest                               
All                14487         28089  42576
0                  14477         26784  41261
1                     10          1305   1315
------------------------------------------------------------------------------------------------------------------------


stacked_barplot(data, 'no_of_special_requests','booking_status')

booking_status          Canceled  Not_Canceled    All
no_of_special_requests                               
All                        14487         28089  42576
0                           8752         10476  19228
1                           4346         11225  15571
2                           1389          4992   6381
3                              0          1230   1230
4                              0           150    150
5                              0            16     16
------------------------------------------------------------------------------------------------------------------------


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 42576 entries, 0 to 56924
Data columns (total 27 columns):
 #   Column                                Non-Null Count  Dtype         
---  ------                                --------------  -----         
 0   no_of_adults                          42576 non-null  int64         
 1   no_of_children                        42576 non-null  int64         
 2   no_of_weekend_nights                  42576 non-null  int64         
 3   no_of_week_nights                     42576 non-null  int64         
 4   type_of_meal_plan                     42576 non-null  category      
 5   required_car_parking_space            42576 non-null  int64         
 6   room_type_reserved                    42576 non-null  category      
 7   lead_time                             42576 non-null  int64         
 8   arrival_year                          42576 non-null  int64         
 9   arrival_month                         42576 non-null  int64         
 10  arrival_date                          42576 non-null  int64         
 11  market_segment_type                   42576 non-null  category      
 12  repeated_guest                        42576 non-null  int64         
 13  no_of_previous_cancellations          42576 non-null  int64         
 14  no_of_previous_bookings_not_canceled  42576 non-null  int64         
 15  avg_price_per_room                    42576 non-null  float64       
 16  no_of_special_requests                42576 non-null  int64         
 17  booking_status                        42576 non-null  category      
 18  lead_time_log_plus_1                  42576 non-null  float64       
 19  avg_price_bin                         42576 non-null  category      
 20  weekend_only_booking                  42576 non-null  int64         
 21  adults_only_booking                   42576 non-null  int64         
 22  month_in_chars                        42576 non-null  category      
 23  full_date                             42541 non-null  datetime64[ns]
 24  day_of_week                           42541 non-null  category      
 25  total_no_of_person                    42576 non-null  int64         
 26  total_no_of_days                      42576 non-null  int64         
dtypes: category(7), datetime64[ns](1), float64(2), int64(17)
memory usage: 8.1 MB


# make  a copy
data_m = data.copy()
colums_for_model = ['type_of_meal_plan', 'required_car_parking_space', 'room_type_reserved', 'lead_time_log_plus_1',
                    'market_segment_type','repeated_guest', 'no_of_previous_cancellations', 
                    'no_of_previous_bookings_not_canceled','avg_price_bin', 'day_of_week', 'booking_status',
                    'total_no_of_days','weekend_only_booking' ,'total_no_of_person', 'adults_only_booking' ]

data_m = data_m[colums_for_model]

# Set y variable to 1 and 0
data_m['booking_status'] = np.where(data_m['booking_status']=='Canceled', 1, 0)


data_m.head(20)


# creating dummy varibles
dummy_data = pd.get_dummies(
    data_m,
    columns=[
        'type_of_meal_plan',
        'room_type_reserved',
        'market_segment_type',
        'avg_price_bin',
        'day_of_week',
    ],
    drop_first=True,
)
dummy_data.head()


dummy_data.shape

(42576, 34)


final_data_for_model = dummy_data.copy()


final_data_for_model.shape

(42576, 34)


X = final_data_for_model.drop("booking_status", axis=1)  # Features
y = final_data_for_model["booking_status"].astype("int64")  # Labels (Target Variable)
# converting target to integers - since some functions might not work with bool type


# Splitting data into training and test set:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
print(X_train.shape, X_test.shape)
print(y_train.shape, y_test.shape)

(29803, 33) (12773, 33)
(29803,) (12773,)


print("Number of rows in train data =", X_train.shape[0])
print("Number of rows in test data =", X_test.shape[0])

Number of rows in train data = 29803
Number of rows in test data = 12773


print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Percentage of classes in training set:
0    0.661074
1    0.338926
Name: booking_status, dtype: float64
Percentage of classes in test set:
0    0.656619
1    0.343381
Name: booking_status, dtype: float64


vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("Series before feature selection: \n\n{}\n".format(vif_series))

Series before feature selection: 

required_car_parking_space               1.065930
lead_time_log_plus_1                     9.405056
repeated_guest                           2.035244
no_of_previous_cancellations             1.514439
no_of_previous_bookings_not_canceled     1.853455
total_no_of_days                         4.631807
weekend_only_booking                     1.387493
total_no_of_person                      22.472374
adults_only_booking                     17.822826
type_of_meal_plan_Meal Plan 2            1.124285
type_of_meal_plan_Meal Plan 3            1.028612
type_of_meal_plan_Not Selected           1.597556
room_type_reserved_Room_Type 2           1.068736
room_type_reserved_Room_Type 3           1.001242
room_type_reserved_Room_Type 4           1.798054
room_type_reserved_Room_Type 5           1.125671
room_type_reserved_Room_Type 6           1.793688
room_type_reserved_Room_Type 7           1.271150
market_segment_type_Complementary        1.672177
market_segment_type_Corporate            5.004920
market_segment_type_Offline             13.175868
market_segment_type_Online              75.597940
avg_price_bin_50-100                    27.370733
avg_price_bin_100-200                   35.136994
avg_price_bin_200-250                    3.084935
avg_price_bin_250-300                    1.417834
avg_price_bin_Above_300                  1.062793
day_of_week_Monday                       2.071357
day_of_week_Saturday                     2.118865
day_of_week_Sunday                       2.205319
day_of_week_Thursday                     1.980328
day_of_week_Tuesday                      2.317663
day_of_week_Wednesday                    2.317117
dtype: float64


# There are different solvers available in Sklearn logistic regression
# The newton-cg solver is faster for high-dimensional data

model = LogisticRegression(solver="newton-cg", random_state=1)
lg = model.fit(X_train, y_train)


# predicting on training set
y_pred_train = lg.predict(X_train)


print("Training set performance:")
print("Accuracy:", accuracy_score(y_train, y_pred_train))
print("Precision:", precision_score(y_train, y_pred_train))
print("Recall:", recall_score(y_train, y_pred_train))
print("F1:", f1_score(y_train, y_pred_train))

Training set performance:
Accuracy: 0.7417374089856725
Precision: 0.6372772955687529
Recall: 0.5524205524205524
F1: 0.5918226653232223


# predicting on the test set
y_pred_test = lg.predict(X_test)


print("Test set performance:")
print("Accuracy:", accuracy_score(y_test, y_pred_test))
print("Precision:", precision_score(y_test, y_pred_test))
print("Recall:", recall_score(y_test, y_pred_test))
print("F1:", f1_score(y_test, y_pred_test))

Test set performance:
Accuracy: 0.7375714397557348
Precision: 0.6413340623291416
Recall: 0.5348837209302325
F1: 0.583291894579811


from sklearn import metrics
cm=metrics.confusion_matrix(y_test, y_pred_test, labels=[1, 0])

df_cm = pd.DataFrame(cm, index = [i for i in ["Actual 1"," Actual 0"]],
                  columns = [i for i in ["Predict 1","Predict 0"]])
plt.figure(figsize = (7,5))
sns.heatmap(df_cm, annot=True,fmt='g')
plt.show()


from statsmodels.tools.sm_exceptions import ConvergenceWarning
warnings.simplefilter('ignore', ConvergenceWarning)

X = final_data_for_model.drop("booking_status", axis=1)
Y = final_data_for_model["booking_status"]

# creating dummy variables
X = pd.get_dummies(X, drop_first=True)

# adding constant

X_sm = sm.add_constant(X)

# splitting in training and test set
X_train_sm, X_test_sm, y_train_sm, y_test_sm = train_test_split(X_sm, Y, test_size=0.3, random_state=1)


X_train_sm.shape

(29803, 34)


logit = sm.Logit(y_train_sm, X_train_sm.astype(float))
lg = logit.fit(
    disp=False
)  # setting disp=False will remove the information on number of iterations

print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                29803
Model:                          Logit   Df Residuals:                    29769
Method:                           MLE   Df Model:                           33
Date:                Sat, 18 Sep 2021   Pseudo R-squ.:                  0.2130
Time:                        04:37:24   Log-Likelihood:                -15019.
converged:                      False   LL-Null:                       -19083.
Covariance Type:            nonrobust   LLR p-value:                     0.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                   -4.2243      0.345    -12.233      0.000      -4.901      -3.548
required_car_parking_space              -1.4871      0.106    -14.080      0.000      -1.694      -1.280
lead_time_log_plus_1                     0.8551      0.015     57.476      0.000       0.826       0.884
repeated_guest                          -2.9969      0.567     -5.290      0.000      -4.107      -1.887
no_of_previous_cancellations             0.3153      0.098      3.214      0.001       0.123       0.507
no_of_previous_bookings_not_canceled    -0.0470      0.063     -0.745      0.457      -0.171       0.077
total_no_of_days                         0.0420      0.008      5.361      0.000       0.027       0.057
weekend_only_booking                     0.3769      0.071      5.299      0.000       0.238       0.516
total_no_of_person                      -0.1097      0.032     -3.432      0.001      -0.172      -0.047
adults_only_booking                     -0.0152      0.064     -0.239      0.811      -0.140       0.109
type_of_meal_plan_Meal Plan 2            0.0701      0.068      1.038      0.299      -0.062       0.203
type_of_meal_plan_Meal Plan 3            1.7764   4598.479      0.000      1.000   -9011.077    9014.630
type_of_meal_plan_Not Selected           0.2911      0.038      7.572      0.000       0.216       0.366
room_type_reserved_Room_Type 2           0.0569      0.108      0.529      0.597      -0.154       0.268
room_type_reserved_Room_Type 3           0.6426      1.105      0.581      0.561      -1.524       2.809
room_type_reserved_Room_Type 4          -0.0485      0.040     -1.222      0.222      -0.126       0.029
room_type_reserved_Room_Type 5          -0.1064      0.101     -1.054      0.292      -0.304       0.091
room_type_reserved_Room_Type 6           0.4316      0.096      4.504      0.000       0.244       0.619
room_type_reserved_Room_Type 7          -0.4956      0.199     -2.491      0.013      -0.886      -0.106
market_segment_type_Complementary      -15.8609    506.946     -0.031      0.975   -1009.457     977.735
market_segment_type_Corporate           -1.0157      0.281     -3.608      0.000      -1.567      -0.464
market_segment_type_Offline             -2.5851      0.267     -9.672      0.000      -3.109      -2.061
market_segment_type_Online              -1.2306      0.262     -4.690      0.000      -1.745      -0.716
avg_price_bin_50-100                     1.2315      0.214      5.752      0.000       0.812       1.651
avg_price_bin_100-200                    1.8918      0.214      8.830      0.000       1.472       2.312
avg_price_bin_200-250                    3.1784      0.236     13.443      0.000       2.715       3.642
avg_price_bin_250-300                    3.9227      0.324     12.121      0.000       3.288       4.557
avg_price_bin_Above_300                  3.2453      0.580      5.599      0.000       2.109       4.381
day_of_week_Monday                       0.0499      0.055      0.913      0.361      -0.057       0.157
day_of_week_Saturday                    -0.0263      0.054     -0.484      0.628      -0.133       0.080
day_of_week_Sunday                       0.0404      0.053      0.759      0.448      -0.064       0.145
day_of_week_Thursday                    -0.0238      0.057     -0.415      0.678      -0.136       0.088
day_of_week_Tuesday                     -0.2085      0.058     -3.616      0.000      -0.321      -0.095
day_of_week_Wednesday                   -0.1212      0.054     -2.236      0.025      -0.227      -0.015
========================================================================================================


vif_values_m0 = checking_vif(X_train_sm)
vif_values_m0


X_train_sm.shape

(29803, 34)


X_train_sm_2 = X_train_sm.drop('market_segment_type_Corporate', axis=1)
X_train_sm_2 = X_train_sm_2.drop('avg_price_bin_50-100', axis=1)
X_train_sm_2 = X_train_sm_2.drop('market_segment_type_Online', axis=1)


X_train_sm_2.shape

(29803, 31)


vif_values_m1 = checking_vif(X_train_sm_2)
vif_values_m1


logit2 = sm.Logit(y_train_sm, X_train_sm_2.astype(float))
lg2 = logit2.fit(
    disp=False
)  # setting disp=False will remove the information on number of iterations

print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                29803
Model:                          Logit   Df Residuals:                    29772
Method:                           MLE   Df Model:                           30
Date:                Sat, 18 Sep 2021   Pseudo R-squ.:                  0.2113
Time:                        04:39:09   Log-Likelihood:                -15051.
converged:                      False   LL-Null:                       -19083.
Covariance Type:            nonrobust   LLR p-value:                     0.000
========================================================================================================
                                           coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------------
const                                   -4.1712      0.123    -33.870      0.000      -4.413      -3.930
required_car_parking_space              -1.4795      0.106    -14.015      0.000      -1.686      -1.273
lead_time_log_plus_1                     0.8452      0.015     57.695      0.000       0.816       0.874
repeated_guest                          -2.7916      0.564     -4.947      0.000      -3.898      -1.686
no_of_previous_cancellations             0.2966      0.098      3.040      0.002       0.105       0.488
no_of_previous_bookings_not_canceled    -0.0466      0.064     -0.730      0.466      -0.172       0.078
total_no_of_days                         0.0436      0.008      5.588      0.000       0.028       0.059
weekend_only_booking                     0.3822      0.071      5.377      0.000       0.243       0.521
total_no_of_person                      -0.1195      0.031     -3.806      0.000      -0.181      -0.058
adults_only_booking                     -0.0208      0.063     -0.328      0.743      -0.145       0.103
type_of_meal_plan_Meal Plan 2            0.0500      0.067      0.744      0.457      -0.082       0.182
type_of_meal_plan_Meal Plan 3            1.6284   8246.574      0.000      1.000   -1.62e+04    1.62e+04
type_of_meal_plan_Not Selected           0.2819      0.038      7.400      0.000       0.207       0.357
room_type_reserved_Room_Type 2           0.0517      0.107      0.482      0.630      -0.158       0.262
room_type_reserved_Room_Type 3           0.6522      1.105      0.590      0.555      -1.514       2.819
room_type_reserved_Room_Type 4          -0.0431      0.040     -1.086      0.278      -0.121       0.035
room_type_reserved_Room_Type 5          -0.0909      0.100     -0.905      0.365      -0.288       0.106
room_type_reserved_Room_Type 6           0.4370      0.096      4.575      0.000       0.250       0.624
room_type_reserved_Room_Type 7          -0.4866      0.198     -2.452      0.014      -0.876      -0.098
market_segment_type_Complementary      -16.9099    889.832     -0.019      0.985   -1760.949    1727.129
market_segment_type_Offline             -1.3626      0.054    -25.308      0.000      -1.468      -1.257
avg_price_bin_100-200                    0.6734      0.034     19.940      0.000       0.607       0.740
avg_price_bin_200-250                    1.9527      0.105     18.601      0.000       1.747       2.158
avg_price_bin_250-300                    2.7000      0.245     11.038      0.000       2.221       3.179
avg_price_bin_Above_300                  2.0422      0.541      3.776      0.000       0.982       3.102
day_of_week_Monday                       0.0489      0.055      0.896      0.370      -0.058       0.156
day_of_week_Saturday                    -0.0209      0.054     -0.385      0.700      -0.127       0.086
day_of_week_Sunday                       0.0408      0.053      0.766      0.444      -0.064       0.145
day_of_week_Thursday                    -0.0250      0.057     -0.437      0.662      -0.137       0.087
day_of_week_Tuesday                     -0.2085      0.058     -3.623      0.000      -0.321      -0.096
day_of_week_Wednesday                   -0.1172      0.054     -2.163      0.031      -0.223      -0.011
========================================================================================================


# initial list of columns
cols = X_train_sm_2.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    X_train_aux = X_train_sm_2[cols]

    # fitting the model
    model = sm.Logit(y_train, X_train_aux).fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = max(p_values)

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break

selected_features = cols
print(selected_features)

['const', 'required_car_parking_space', 'lead_time_log_plus_1', 'repeated_guest', 'no_of_previous_cancellations', 'total_no_of_days', 'weekend_only_booking', 'total_no_of_person', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Offline', 'avg_price_bin_100-200', 'avg_price_bin_200-250', 'avg_price_bin_250-300', 'avg_price_bin_Above_300', 'day_of_week_Tuesday', 'day_of_week_Wednesday']


# creating a new training set
X_train3 = X_train_sm_2[selected_features].astype(float)


logit3 = sm.Logit(y_train, X_train3)
lg3 = logit3.fit(disp=False)
print(lg3.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                29803
Model:                          Logit   Df Residuals:                    29785
Method:                           MLE   Df Model:                           17
Date:                Sat, 18 Sep 2021   Pseudo R-squ.:                  0.2100
Time:                        04:45:01   Log-Likelihood:                -15077.
converged:                       True   LL-Null:                       -19083.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                             -4.2226      0.081    -51.838      0.000      -4.382      -4.063
required_car_parking_space        -1.4762      0.105    -14.000      0.000      -1.683      -1.270
lead_time_log_plus_1               0.8503      0.015     58.454      0.000       0.822       0.879
repeated_guest                    -3.1261      0.517     -6.050      0.000      -4.139      -2.113
no_of_previous_cancellations       0.2826      0.097      2.911      0.004       0.092       0.473
total_no_of_days                   0.0434      0.008      5.614      0.000       0.028       0.059
weekend_only_booking               0.3753      0.071      5.293      0.000       0.236       0.514
total_no_of_person                -0.1191      0.027     -4.446      0.000      -0.172      -0.067
type_of_meal_plan_Not Selected     0.3004      0.036      8.382      0.000       0.230       0.371
room_type_reserved_Room_Type 6     0.4752      0.088      5.383      0.000       0.302       0.648
room_type_reserved_Room_Type 7    -0.4826      0.193     -2.498      0.012      -0.861      -0.104
market_segment_type_Offline       -1.3442      0.053    -25.387      0.000      -1.448      -1.240
avg_price_bin_100-200              0.6758      0.032     21.100      0.000       0.613       0.739
avg_price_bin_200-250              1.9529      0.101     19.306      0.000       1.755       2.151
avg_price_bin_250-300              2.7254      0.242     11.239      0.000       2.250       3.201
avg_price_bin_Above_300            2.0890      0.540      3.867      0.000       1.030       3.148
day_of_week_Tuesday               -0.2150      0.045     -4.830      0.000      -0.302      -0.128
day_of_week_Wednesday             -0.1257      0.040     -3.140      0.002      -0.204      -0.047
==================================================================================================


# converting coefficients to odds
odds = np.exp(lg3.params)

# finding the percentage change
perc_change_odds = (np.exp(lg3.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index=X_train3.columns).T


# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


# creating confusion matrix
confusion_matrix_statsmodels(lg3, X_train3, y_train)


log_reg_model_train_perf = model_performance_classification_statsmodels(
    lg3, X_train3, y_train
)

print("Training performance:")
log_reg_model_train_perf

Training performance:


logit_roc_auc_train = roc_auc_score(y_train, lg3.predict(X_train3))
fpr, tpr, thresholds = roc_curve(y_train, lg3.predict(X_train3))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(y_train, lg3.predict(X_train3))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.28767741679730013


# creating confusion matrix
confusion_matrix_statsmodels(
    lg3, X_train3, y_train, threshold=optimal_threshold_auc_roc
)


# checking model performance for this model
log_reg_model_train_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg3, X_train3, y_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
log_reg_model_train_perf_threshold_auc_roc

Training performance:


logit_roc_auc_train = roc_auc_score(y_train, lg3.predict(X_train3))
fpr, tpr, thresholds = roc_curve(y_train, lg3.predict(X_train3))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


y_scores = lg3.predict(X_train3)
prec, rec, tre = precision_recall_curve(y_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()


# setting the threshold
optimal_threshold_curve = 0.45


# creating confusion matrix
confusion_matrix_statsmodels(lg3, X_train3, y_train, threshold=optimal_threshold_curve)


log_reg_model_train_perf_threshold_curve = model_performance_classification_statsmodels(
    lg3, X_train3, y_train, threshold=optimal_threshold_curve
)
print("Training performance:")
log_reg_model_train_perf_threshold_curve

Training performance:


X_test3 = X_test_sm[X_train3.columns].astype(float)


# creating confusion matrix
confusion_matrix_statsmodels(lg3, X_test3, y_test)


# creating confusion matrix
confusion_matrix_statsmodels(lg3, X_test3, y_test, threshold=optimal_threshold_auc_roc)


log_reg_model_test_perf = model_performance_classification_statsmodels(
    lg3, X_test3, y_test
)

print("Test performance:")
log_reg_model_test_perf

Test performance:


# checking model performance for this model
log_reg_model_test_perf_threshold_auc_roc = model_performance_classification_statsmodels(
    lg3, X_test3, y_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
log_reg_model_test_perf_threshold_auc_roc

Test performance:


log_reg_model_test_perf_threshold_curve = model_performance_classification_statsmodels(
    lg3, X_test3, y_test, threshold=optimal_threshold_curve
)
print("Test performance:")
log_reg_model_test_perf_threshold_curve

Test performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        log_reg_model_train_perf.T,
        log_reg_model_train_perf_threshold_auc_roc.T,
        log_reg_model_train_perf_threshold_curve.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression sklearn",
    "Logistic Regression-0.28 Threshold",
    "Logistic Regression-0.45 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


##  Function to calculate recall score
def get_recall_score(model, predictors, target):
    """
    model: classifier
    predictors: independent variables
    target: dependent variable
    
    """
    prediction = model.predict(predictors)
    return recall_score(target, prediction)


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


model = DecisionTreeClassifier(
    criterion="gini", random_state=1
)


model.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)


confusion_matrix_sklearn(model, X_train, y_train)


decision_tree_perf_train = get_recall_score(model, X_train, y_train)

print("Recall Score:", decision_tree_perf_train)

Recall Score: 0.8857538857538858


confusion_matrix_sklearn(model, X_test, y_test)


decision_tree_perf_test = get_recall_score(model, X_test, y_test)
print("Recall Score:", decision_tree_perf_test)

Recall Score: 0.5471956224350205


print("Accuracy on training set : ",model.score(X_train, y_train))
print("Accuracy on test set : ",model.score(X_test, y_test))

Accuracy on training set :  0.9589303090292923
Accuracy on test set :  0.7068034134502467


## creating a list of column names
feature_names = X_train.columns.to_list()


print(tree.export_text(model, feature_names=feature_names, show_weights=True))

|--- lead_time_log_plus_1 <= 5.02
|   |--- lead_time_log_plus_1 <= 2.92
|   |   |--- lead_time_log_plus_1 <= 1.87
|   |   |   |--- total_no_of_days <= 9.50
|   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.24
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Complementary <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Complementary >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [93.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [115.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.24
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [59.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |--- weights: [583.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 0.35
|   |   |   |   |   |   |   |--- total_no_of_days <= 3.00
|   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  3.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  0.35
|   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.70
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.70
|   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.04
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.04
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |--- total_no_of_days >  9.50
|   |   |   |   |--- total_no_of_days <= 17.50
|   |   |   |   |   |--- avg_price_bin_100-200 <= 0.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.50
|   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 15.00
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- total_no_of_days >  15.00
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_bin_100-200 >  0.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.24
|   |   |   |   |   |   |   |--- no_of_previous_bookings_not_canceled <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 0.35
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  0.35
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_previous_bookings_not_canceled >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.24
|   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- total_no_of_days >  17.50
|   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |--- lead_time_log_plus_1 >  1.87
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- total_no_of_days <= 13.50
|   |   |   |   |   |--- avg_price_bin_Above_300 <= 0.50
|   |   |   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.86
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.86
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |--- weights: [190.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- avg_price_bin_Above_300 >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- total_no_of_days >  13.50
|   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |   |   |--- avg_price_bin_100-200 <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_Above_300 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_Above_300 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_bin_100-200 >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 7.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.35
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.35
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  7.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.42
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.42
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- weights: [111.00, 0.00] class: 0
|   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.86
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.86
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 2.60
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  2.60
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_person <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_person >  4.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |--- lead_time_log_plus_1 >  2.92
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time_log_plus_1 <= 4.53
|   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |--- avg_price_bin_Above_300 <= 0.50
|   |   |   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [144.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.17
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.02
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.02
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [30.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.17
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.45
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.32
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.32
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.45
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- avg_price_bin_Above_300 >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 3.82
|   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.35
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_50-100 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_bin_50-100 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.35
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.42
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.42
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [20.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.07
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.07
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.15
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |--- no_of_previous_cancellations <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [53.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_previous_cancellations >  2.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.30
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.30
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- lead_time_log_plus_1 >  3.82
|   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.31
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_previous_bookings_not_canceled <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- no_of_previous_bookings_not_canceled >  5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.31
|   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- lead_time_log_plus_1 >  4.53
|   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |--- avg_price_bin_100-200 <= 0.50
|   |   |   |   |   |   |--- avg_price_bin_50-100 <= 0.50
|   |   |   |   |   |   |   |--- weights: [23.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_bin_50-100 >  0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.86
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.86
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.89
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [21.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.89
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.91
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.91
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.64
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.64
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.68
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |--- avg_price_bin_100-200 >  0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.74
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.74
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.74
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.74
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |--- total_no_of_days <= 12.00
|   |   |   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_bin_50-100 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_bin_50-100 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.69
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.64
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.64
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.66
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.66
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.69
|   |   |   |   |   |   |   |   |   |--- weights: [38.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- total_no_of_days >  12.00
|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 4.53
|   |   |   |   |   |   |--- total_no_of_days <= 12.50
|   |   |   |   |   |   |   |--- avg_price_bin_100-200 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 28
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.35
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.35
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 34
|   |   |   |   |   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_bin_100-200 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.62
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.24
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.24
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 27
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.62
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 37
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.14
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.14
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |--- total_no_of_days >  12.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.16
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.02
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.02
|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.16
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 31.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time_log_plus_1 >  4.53
|   |   |   |   |   |   |--- total_no_of_days <= 9.50
|   |   |   |   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 33
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 30
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 25
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.98
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 26
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |--- total_no_of_days >  9.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 12.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.90
|   |   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.90
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  12.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 4.46
|   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.68
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.68
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.84
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.84
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.65
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.65
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.08
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.08
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.42
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 20
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.42
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 22.00] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 3.64
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  3.64
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.27
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.27
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- lead_time_log_plus_1 >  4.46
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.51
|   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.49
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.49
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.51
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.75
|   |   |   |   |   |   |   |   |--- weights: [0.00, 49.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.75
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.77
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.77
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 26.00] class: 1
|   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |--- room_type_reserved_Room_Type 7 <= 0.50
|   |   |   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |   |   |--- weights: [357.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 4.52
|   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  4.52
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- room_type_reserved_Room_Type 7 >  0.50
|   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|--- lead_time_log_plus_1 >  5.02
|   |--- avg_price_bin_50-100 <= 0.50
|   |   |--- avg_price_bin_100-200 <= 0.50
|   |   |   |--- avg_price_bin_200-250 <= 0.50
|   |   |   |   |--- avg_price_bin_250-300 <= 0.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 6.14
|   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |--- avg_price_bin_Above_300 <= 0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 11.00
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.66
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.66
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- total_no_of_days >  11.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_bin_Above_300 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |--- lead_time_log_plus_1 >  6.14
|   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |--- avg_price_bin_250-300 >  0.50
|   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |--- avg_price_bin_200-250 >  0.50
|   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 62.00] class: 1
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.14
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.10
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.10
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.14
|   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.32
|   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.32
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.34
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.34
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- avg_price_bin_100-200 >  0.50
|   |   |   |--- lead_time_log_plus_1 <= 5.25
|   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.05
|   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 59.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.03
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.03
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.05
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 132.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.08
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 99.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.08
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  3.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 5.50
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.10
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.08
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.08
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.10
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.15
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.15
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  5.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.14
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.14
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.13
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.09
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.09
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.13
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 67.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.15
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 19.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.15
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.15
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.15
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.16
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.03
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.03
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.04
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- lead_time_log_plus_1 >  5.25
|   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.55
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 58.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.55
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.63
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.63
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.64
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.64
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.34
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.33
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.34
|   |   |   |   |   |   |   |   |--- weights: [0.00, 73.00] class: 1
|   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.73
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.41
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.41
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.73
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 7.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.54
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.54
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.78
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.25
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.25
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.36
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.36
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  7.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 35.00] class: 1
|   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 42.00] class: 1
|   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |--- total_no_of_person <= 3.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.83
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.41
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.41
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.51
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 18.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.51
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.53
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.53
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.83
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- total_no_of_person >  3.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.44
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.44
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.55
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.55
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |--- avg_price_bin_50-100 >  0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time_log_plus_1 <= 5.62
|   |   |   |   |--- lead_time_log_plus_1 <= 5.45
|   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |--- total_no_of_days <= 13.00
|   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.36
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.36
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.36
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.36
|   |   |   |   |   |   |   |   |   |--- weights: [33.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.15
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.15
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- total_no_of_days >  13.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |--- lead_time_log_plus_1 >  5.45
|   |   |   |   |   |--- lead_time_log_plus_1 <= 5.58
|   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.48
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.48
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.56
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.56
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.48
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.48
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- lead_time_log_plus_1 >  5.58
|   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.61
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.61
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |--- lead_time_log_plus_1 >  5.62
|   |   |   |   |--- lead_time_log_plus_1 <= 5.90
|   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.83
|   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.83
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.86
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.86
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.63
|   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.63
|   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.65
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.65
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |--- lead_time_log_plus_1 >  5.90
|   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |--- weights: [0.00, 23.00] class: 1
|   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |--- day_of_week_Sunday <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.97
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 2.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.97
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- day_of_week_Sunday >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time_log_plus_1 <= 5.30
|   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 5.17
|   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.05
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.08
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.08
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.16
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.12
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.08
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.08
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.12
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.16
|   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time_log_plus_1 >  5.17
|   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.20
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.20
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 24.00] class: 1
|   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.20
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.20
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.27
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.27
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 5.27
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.25
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.15
|   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.09
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.15
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.20
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.20
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.25
|   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.26
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.26
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.17
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.17
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.26
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.18
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.18
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.26
|   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time_log_plus_1 >  5.27
|   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.28
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.27
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.27
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.28
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.29
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.29
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.29
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.29
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.28
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.28
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- adults_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |--- lead_time_log_plus_1 >  5.30
|   |   |   |   |--- total_no_of_days <= 5.50
|   |   |   |   |   |--- lead_time_log_plus_1 <= 5.73
|   |   |   |   |   |   |--- day_of_week_Thursday <= 0.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- weekend_only_booking >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.37
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.37
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.43
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.43
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_person >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.67
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.67
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |--- day_of_week_Thursday >  0.50
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.65
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.37
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.36
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.36
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.37
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 34.00] class: 1
|   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.47
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.40
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.40
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.47
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.65
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- lead_time_log_plus_1 >  5.73
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 54.00] class: 1
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 15.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 13.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Tuesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  2.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Wednesday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |--- total_no_of_days <= 3.50
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.88
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.88
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- total_no_of_days >  3.50
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  4.50
|   |   |   |   |   |   |   |   |   |--- total_no_of_person <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_person >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |--- total_no_of_days >  5.50
|   |   |   |   |   |--- total_no_of_days <= 11.50
|   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.43
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.34
|   |   |   |   |   |   |   |   |--- total_no_of_days <= 10.50
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- day_of_week_Saturday >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- day_of_week_Monday >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- total_no_of_days >  10.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.34
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.40
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.34
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.34
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.34
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.40
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.43
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.46
|   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.46
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.82
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.70
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.70
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 <= 5.72
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.72
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time_log_plus_1 >  5.82
|   |   |   |   |   |   |   |   |   |--- total_no_of_days <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |   |--- total_no_of_days >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- total_no_of_days >  11.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0


importances = model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


estimator = DecisionTreeClassifier(random_state=1,class_weight={0: 0.34, 1: 0.66})

# Grid of parameters to choose from

parameters = {'max_depth': np.arange(1,10), 
              'min_samples_leaf': [1, 2, 5, 7, 10,15,20],
              'max_leaf_nodes' : [2, 3, 5, 10],
              'min_impurity_decrease': [0.001,0.01,0.1]
             }

# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
estimator.fit(X_train, y_train)

DecisionTreeClassifier(class_weight={0: 0.34, 1: 0.66}, max_depth=3,
                       max_leaf_nodes=5, min_impurity_decrease=0.001,
                       random_state=1)


confusion_matrix_sklearn(estimator, X_train, y_train)


decision_tree_tune_perf_train = get_recall_score(estimator, X_train, y_train)
print("Recall Score:", decision_tree_tune_perf_train)

Recall Score: 0.8959508959508959


confusion_matrix_sklearn(estimator, X_test, y_test)


decision_tree_tune_perf_test = get_recall_score(estimator, X_test, y_test)
print("Recall Score:", decision_tree_tune_perf_test)

Recall Score: 0.895576835385317


# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {
    "max_depth": [5, 10, 15, None],
    "criterion": ["entropy", "gini"],
    "splitter": ["best", "random"],
    "min_impurity_decrease": [0.00001, 0.0001, 0.01],
}

# Type of scoring used to compare parameter combinations
acc_scorer = metrics.make_scorer(metrics.recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator2 = grid_obj.best_estimator_

# Fit the best algorithm to the data. 
estimator2.fit(X_train, y_train)

DecisionTreeClassifier(criterion='entropy', min_impurity_decrease=1e-05,
                       random_state=1)


confusion_matrix_sklearn(estimator2, X_train, y_train)


decision_tree_tune_perf_train2 = get_recall_score(estimator2, X_train, y_train)
print("Recall Score:", decision_tree_tune_perf_train2)

Recall Score: 0.8744678744678744


confusion_matrix_sklearn(estimator2, X_test, y_test)


decision_tree_tune_perf_test2 = get_recall_score(estimator2, X_test, y_test)
print("Recall Score:", decision_tree_tune_perf_test2)

Recall Score: 0.5362517099863201


clf = DecisionTreeClassifier(random_state=1, )
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities


pd.DataFrame(path)


fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()


clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha
    )
    clf.fit(X_train, y_train)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.07447795787334116


clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()


recall_train = []
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train)
    recall_train.append(values_train)


recall_test = []
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = recall_score(y_test, pred_test)
    recall_test.append(values_test)


train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(
    ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post",
)
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()


# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=1.3421467637486154e-05, random_state=1)


best_model.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=1.3421467637486154e-05, random_state=1)


confusion_matrix_sklearn(best_model, X_train, y_train)


decision_tree_postpruned_perf_best_train = get_recall_score(best_model, X_train, y_train)
print("Recall Score:", decision_tree_postpruned_perf_best_train)

Recall Score: 0.9046629046629047


confusion_matrix_sklearn(best_model, X_test, y_test)


decision_tree_postpruned_perf_best_test = get_recall_score(best_model, X_test, y_test)
print("Recall Score:", decision_tree_postpruned_perf_best_test)

Recall Score: 0.5617875056999544


best_model2 = DecisionTreeClassifier(
    ccp_alpha=0.000000001,  random_state=1
)
best_model2.fit(X_train, y_train)

DecisionTreeClassifier(ccp_alpha=1e-09, random_state=1)


decision_tree_postpruned_perf_train = get_recall_score(best_model2, X_train, y_train)
print("Recall Score:", decision_tree_postpruned_perf_train)

Recall Score: 0.8857538857538858


confusion_matrix_sklearn(best_model2, X_test, y_test)


decision_tree_postpruned_perf_test = get_recall_score(best_model2, X_test, y_test)
print("Recall Score:", decision_tree_postpruned_perf_test)

Recall Score: 0.5471956224350205


# training performance comparison

models_train_comp_df = pd.DataFrame(
    [
        decision_tree_perf_train,
        decision_tree_tune_perf_train,
        decision_tree_postpruned_perf_best_train,
        decision_tree_postpruned_perf_train,
    ],
    columns=["Recall on training set"],
)

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.DataFrame(
    [
        decision_tree_perf_test,
        decision_tree_tune_perf_test,
        decision_tree_postpruned_perf_best_test,
        decision_tree_postpruned_perf_test,
    ],
    columns=["Recall on testing set"],
)

print("Test performance comparison:")
models_test_comp_df

Test performance comparison:

	const	required_car_parking_space	lead_time_log_plus_1	repeated_guest	no_of_previous_cancellations	total_no_of_days	weekend_only_booking	total_no_of_person	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Offline	avg_price_bin_100-200	avg_price_bin_200-250	avg_price_bin_250-300	avg_price_bin_Above_300	day_of_week_Tuesday	day_of_week_Wednesday
Odds	0.014660	0.228501	2.340315	0.043888	1.32661	1.044401	1.455463	0.887718	1.350433	1.608287	0.617161	0.260756	1.965695	7.048901	15.262310	8.077027	0.806553	0.881883
Change_odd%	-98.534018	-77.149861	134.031462	-95.611192	32.66101	4.440124	45.546311	-11.228242	35.043308	60.828730	-38.283923	-73.924413	96.569481	604.890121	1426.230974	707.702694	-19.344682	-11.811669

	Logistic Regression sklearn	Logistic Regression-0.28 Threshold	Logistic Regression-0.45 Threshold
Accuracy	0.741536	0.680703	0.731235
Recall	0.553807	0.826849	0.617266
Precision	0.636405	0.518146	0.600732
F1	0.592240	0.637071	0.608887

	ccp_alphas	impurities
0	0.000000	0.045193
1	0.000000	0.045193
2	0.000000	0.045193
3	0.000000	0.045193
4	0.000000	0.045193
...	...	...
3591	0.004814	0.337216
3592	0.010504	0.347720
3593	0.011955	0.359675
3594	0.013957	0.373632
3595	0.074478	0.448110

	Recall on training set
0	0.885754
1	0.895951
2	0.904663
3	0.885754

	Recall on testing set
0	0.547196
1	0.895577
2	0.561788
3	0.547196

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	required_car_parking_space	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status
10043	1	0	0	2	Not Selected	0	Room_Type 1	37	2018	10	27	Online	0	0	0	109.00	1	Not_Canceled
39715	2	1	2	3	Meal Plan 1	0	Room_Type 1	104	2019	4	6	Online	0	0	0	101.15	0	Not_Canceled
30095	2	0	0	1	Not Selected	0	Room_Type 1	20	2019	4	20	Online	0	0	0	115.00	0	Not_Canceled
11327	1	0	2	2	Meal Plan 1	0	Room_Type 1	162	2018	10	14	Online	0	0	0	115.00	0	Canceled
45593	1	0	2	6	Meal Plan 1	0	Room_Type 1	115	2018	2	27	Offline	0	0	0	64.75	0	Canceled
19258	1	0	2	1	Meal Plan 1	0	Room_Type 5	2	2019	5	7	Aviation	1	0	1	125.00	0	Not_Canceled
5654	2	0	1	1	Meal Plan 1	0	Room_Type 1	0	2017	9	21	Corporate	0	0	0	65.00	0	Not_Canceled
15474	2	1	1	3	Meal Plan 1	0	Room_Type 1	57	2018	8	15	Online	0	0	0	152.10	0	Not_Canceled
16553	3	0	0	2	Meal Plan 1	0	Room_Type 4	73	2018	9	6	Online	0	0	0	168.30	2	Not_Canceled
17911	2	0	0	2	Meal Plan 1	0	Room_Type 4	6	2017	12	31	Online	0	0	0	137.00	1	Not_Canceled
16680	2	0	2	2	Meal Plan 1	0	Room_Type 4	139	2018	7	31	Online	0	0	0	114.33	0	Canceled
47788	1	0	0	1	Meal Plan 1	0	Room_Type 5	43	2019	4	27	Online	0	0	0	174.00	1	Canceled
56495	2	0	1	1	Meal Plan 1	0	Room_Type 1	275	2018	6	4	Offline	0	0	0	62.80	0	Canceled
42645	2	0	2	2	Meal Plan 1	0	Room_Type 1	252	2019	1	1	Online	0	0	0	77.35	1	Canceled
2616	2	1	1	1	Meal Plan 1	0	Room_Type 1	161	2018	7	25	Online	0	0	0	121.50	0	Canceled
51042	2	0	1	1	Meal Plan 1	0	Room_Type 1	180	2018	5	2	Offline	0	0	0	100.00	1	Not_Canceled
48129	1	0	0	1	Meal Plan 1	0	Room_Type 1	41	2018	5	24	Corporate	0	0	0	89.00	0	Canceled
33556	1	0	0	1	Meal Plan 1	0	Room_Type 1	1	2017	11	19	Corporate	0	0	0	65.00	0	Not_Canceled
35159	2	0	1	1	Not Selected	0	Room_Type 1	3	2018	3	21	Online	0	0	0	89.00	0	Not_Canceled
6151	2	0	2	3	Meal Plan 1	0	Room_Type 1	265	2018	10	9	Online	0	0	0	100.30	0	Canceled
33590	2	0	0	4	Meal Plan 1	0	Room_Type 5	157	2019	7	12	Online	0	0	0	143.10	2	Canceled
8581	2	0	1	4	Meal Plan 1	0	Room_Type 1	114	2018	9	12	Online	0	0	0	129.60	1	Not_Canceled
51925	2	0	2	1	Meal Plan 2	0	Room_Type 1	112	2019	5	28	Online	0	0	0	137.21	1	Not_Canceled
35613	2	0	2	1	Meal Plan 1	0	Room_Type 1	76	2019	4	2	Offline	0	0	0	75.00	0	Not_Canceled
12548	2	0	0	1	Not Selected	0	Room_Type 1	4	2018	12	15	Online	0	0	0	78.00	2	Not_Canceled
45519	2	0	0	2	Meal Plan 1	1	Room_Type 1	145	2018	6	28	Online	0	0	0	105.30	1	Not_Canceled
46898	2	0	2	3	Meal Plan 1	0	Room_Type 1	131	2019	4	23	Online	0	0	0	99.00	2	Not_Canceled
51323	2	0	0	2	Meal Plan 1	0	Room_Type 1	260	2019	3	10	Online	0	0	0	90.00	0	Canceled
24353	2	1	0	3	Meal Plan 2	0	Room_Type 1	8	2017	9	1	Offline	0	0	0	134.75	2	Not_Canceled
35970	2	0	0	2	Meal Plan 1	1	Room_Type 1	229	2019	8	2	Online	0	0	0	139.50	1	Canceled
4356	2	0	1	2	Meal Plan 1	0	Room_Type 1	191	2018	6	3	Online	0	0	0	132.00	0	Canceled
11007	2	0	0	1	Meal Plan 1	0	Room_Type 1	34	2019	1	18	Offline	0	0	0	75.00	0	Not_Canceled
44367	2	0	1	4	Not Selected	0	Room_Type 1	37	2018	8	22	Online	0	0	0	105.33	1	Not_Canceled
27919	2	0	0	3	Meal Plan 1	0	Room_Type 1	55	2018	4	6	Online	0	0	0	118.33	1	Canceled
15220	2	0	1	1	Not Selected	0	Room_Type 1	184	2018	8	13	Online	0	0	0	94.50	0	Canceled
17071	2	0	0	3	Not Selected	0	Room_Type 1	107	2019	8	3	Online	0	0	0	125.00	2	Canceled
6026	1	0	1	0	Not Selected	0	Room_Type 1	3	2018	12	19	Online	1	0	2	55.60	0	Not_Canceled
27649	1	0	0	2	Not Selected	0	Room_Type 1	52	2019	6	28	Online	0	0	0	120.00	1	Canceled
30606	1	0	2	2	Meal Plan 1	0	Room_Type 4	3	2018	9	18	Aviation	0	0	0	110.00	0	Not_Canceled
13171	2	0	2	7	Meal Plan 1	0	Room_Type 1	193	2018	7	13	Online	0	0	0	90.95	0	Canceled
18907	2	0	0	4	Meal Plan 1	0	Room_Type 1	204	2017	8	12	Online	0	0	0	76.50	3	Not_Canceled
46980	0	2	2	1	Meal Plan 1	0	Room_Type 2	194	2018	7	9	Online	0	0	0	86.63	0	Canceled
27126	3	0	2	3	Meal Plan 1	0	Room_Type 4	171	2019	7	15	Online	0	0	0	152.10	1	Canceled
36062	2	0	0	5	Not Selected	0	Room_Type 1	32	2018	10	25	Online	0	0	0	109.00	1	Not_Canceled
28844	2	0	0	3	Meal Plan 1	0	Room_Type 1	16	2018	12	8	Online	0	0	0	108.67	0	Not_Canceled
6599	2	0	2	1	Meal Plan 1	1	Room_Type 1	9	2018	8	14	Online	0	0	0	125.27	1	Not_Canceled
30338	1	0	1	2	Meal Plan 1	0	Room_Type 1	180	2018	10	10	Offline	0	0	0	120.00	0	Canceled
48414	2	0	2	4	Meal Plan 1	0	Room_Type 1	19	2018	11	13	Offline	0	0	0	68.00	1	Not_Canceled
32639	1	0	1	1	Meal Plan 1	0	Room_Type 1	85	2018	12	3	Online	0	0	0	98.00	0	Canceled
20174	2	0	2	4	Meal Plan 1	0	Room_Type 4	54	2018	10	30	Online	0	0	0	111.00	1	Canceled
23735	1	0	0	1	Meal Plan 1	0	Room_Type 1	2	2018	1	16	Complementary	0	0	0	0.00	1	Not_Canceled
38581	1	2	2	4	Meal Plan 1	0	Room_Type 6	81	2018	10	15	Online	0	0	0	190.80	0	Canceled
26673	2	1	1	4	Meal Plan 1	0	Room_Type 1	139	2018	7	11	Online	0	0	0	116.45	0	Canceled
1907	2	0	0	4	Meal Plan 1	0	Room_Type 1	166	2019	6	27	Online	0	0	0	117.00	2	Canceled
34496	2	0	0	2	Not Selected	0	Room_Type 1	32	2019	6	21	Online	0	0	0	140.00	1	Canceled
35283	2	1	0	2	Meal Plan 1	0	Room_Type 1	38	2018	3	25	Online	0	0	0	143.10	2	Not_Canceled
18474	2	0	1	2	Meal Plan 1	0	Room_Type 4	89	2019	5	1	Offline	0	0	0	99.57	0	Not_Canceled
43503	2	0	0	1	Meal Plan 1	0	Room_Type 1	40	2018	6	9	Online	0	0	0	108.90	1	Not_Canceled
16431	2	0	2	2	Meal Plan 1	0	Room_Type 1	105	2019	3	31	Online	0	0	0	110.70	1	Canceled
13151	2	0	4	11	Meal Plan 1	1	Room_Type 1	103	2018	6	28	Online	0	0	0	116.38	0	Canceled
15265	2	0	2	3	Meal Plan 1	0	Room_Type 1	108	2018	5	7	Online	0	0	0	74.54	0	Not_Canceled
17374	2	0	2	3	Meal Plan 1	0	Room_Type 1	50	2018	4	1	Online	0	0	0	101.31	0	Canceled
53052	2	0	0	2	Not Selected	0	Room_Type 1	212	2019	6	9	Online	0	0	0	99.00	1	Canceled
46526	1	1	0	3	Meal Plan 1	0	Room_Type 4	127	2018	12	27	Online	0	0	0	84.05	1	Canceled
10477	2	0	0	3	Meal Plan 1	0	Room_Type 4	93	2019	5	11	Online	0	0	0	105.84	0	Canceled
40208	2	0	1	3	Meal Plan 1	0	Room_Type 4	90	2019	3	27	Online	0	0	0	100.30	1	Canceled
26954	1	0	2	3	Meal Plan 1	0	Room_Type 1	31	2019	4	21	Offline	0	0	0	75.00	1	Not_Canceled
2501	2	0	0	3	Meal Plan 1	0	Room_Type 4	133	2018	6	8	Online	0	0	0	90.95	0	Canceled
9729	2	0	1	4	Not Selected	0	Room_Type 1	103	2018	7	6	Offline	0	0	0	78.20	0	Not_Canceled
11513	2	0	1	0	Meal Plan 1	0	Room_Type 1	125	2017	7	26	Online	0	0	0	85.50	0	Canceled
27722	2	0	1	2	Not Selected	0	Room_Type 1	54	2018	11	11	Online	0	0	0	79.20	0	Canceled
53536	2	0	2	2	Meal Plan 1	0	Room_Type 1	257	2018	7	31	Online	0	0	0	90.95	0	Canceled
53457	2	1	0	3	Meal Plan 1	0	Room_Type 1	34	2018	3	22	Online	0	0	0	115.20	0	Canceled
14643	2	1	0	2	Meal Plan 1	0	Room_Type 1	2	2018	12	28	Online	0	0	0	142.00	2	Not_Canceled
3442	1	0	0	2	Meal Plan 2	0	Room_Type 1	63	2017	9	4	Offline	0	0	0	80.50	0	Not_Canceled
52193	2	1	2	2	Meal Plan 1	0	Room_Type 1	251	2019	6	30	Online	0	0	0	127.58	2	Canceled
20618	2	0	1	0	Not Selected	0	Room_Type 1	79	2019	2	27	Online	0	0	0	79.20	1	Canceled
19822	2	0	0	3	Meal Plan 1	0	Room_Type 4	66	2019	6	20	Online	0	0	0	170.00	1	Not_Canceled
56837	2	0	1	3	Meal Plan 1	0	Room_Type 4	53	2019	3	16	Online	0	0	0	100.30	3	Not_Canceled
8809	1	0	2	5	Meal Plan 1	0	Room_Type 1	26	2018	12	6	Online	0	0	0	88.40	3	Not_Canceled
33216	2	0	0	1	Meal Plan 1	0	Room_Type 4	0	2019	4	15	Offline	0	0	0	144.00	0	Not_Canceled
45899	2	0	2	3	Meal Plan 1	0	Room_Type 1	359	2018	10	14	Offline	0	0	0	78.00	1	Canceled
15853	2	0	2	1	Not Selected	0	Room_Type 1	31	2018	3	5	Online	0	0	0	76.10	0	Not_Canceled
17459	2	0	0	2	Not Selected	0	Room_Type 1	121	2019	3	31	Online	0	0	0	89.10	0	Not_Canceled
56810	2	0	2	4	Meal Plan 2	0	Room_Type 1	12	2019	1	28	Offline	0	0	0	105.00	0	Not_Canceled
27985	2	0	0	2	Meal Plan 1	0	Room_Type 1	97	2019	3	14	Offline	0	0	0	85.00	0	Canceled
4571	2	0	2	1	Meal Plan 1	0	Room_Type 1	15	2018	1	24	Online	0	0	0	68.99	1	Not_Canceled
19578	1	0	0	1	Not Selected	0	Room_Type 1	14	2019	2	3	Online	0	0	0	88.00	1	Not_Canceled
15097	2	0	0	2	Meal Plan 1	0	Room_Type 1	238	2018	9	21	Online	0	0	0	126.90	1	Canceled
50488	3	0	0	2	Meal Plan 1	0	Room_Type 4	33	2019	3	23	Offline	0	0	0	130.40	0	Not_Canceled
33882	2	0	1	3	Meal Plan 1	0	Room_Type 4	74	2019	4	3	Online	0	0	0	97.02	0	Not_Canceled
50273	2	0	0	3	Not Selected	0	Room_Type 1	65	2019	3	14	Online	0	0	0	79.20	0	Not_Canceled
9130	3	0	0	2	Meal Plan 1	1	Room_Type 4	38	2018	7	19	Online	0	0	0	159.30	1	Not_Canceled
13706	2	0	1	0	Meal Plan 1	0	Room_Type 1	34	2019	6	12	Online	0	0	0	140.00	2	Not_Canceled
40018	2	0	0	2	Not Selected	0	Room_Type 1	0	2018	10	21	Online	0	0	0	63.76	0	Not_Canceled
13061	2	0	1	0	Meal Plan 1	0	Room_Type 1	34	2019	7	3	Online	0	0	0	103.20	1	Not_Canceled
43294	1	0	2	0	Meal Plan 1	0	Room_Type 1	2	2018	7	10	Corporate	1	0	7	65.00	0	Not_Canceled
54337	2	0	0	1	Not Selected	0	Room_Type 1	244	2018	9	3	Online	0	0	0	85.50	1	Canceled
17721	2	0	2	5	Meal Plan 1	0	Room_Type 4	183	2019	5	20	Online	0	0	0	139.11	0	Canceled
32381	2	0	0	2	Not Selected	0	Room_Type 1	207	2019	2	24	Online	0	0	0	79.20	3	Not_Canceled
26783	1	0	0	1	Meal Plan 1	0	Room_Type 1	98	2018	9	21	Corporate	1	1	10	65.00	1	Not_Canceled
26462	2	0	0	3	Meal Plan 1	0	Room_Type 4	125	2019	6	22	Online	0	0	0	126.00	0	Not_Canceled
3613	1	1	1	2	Meal Plan 2	0	Room_Type 1	129	2019	6	26	Online	0	0	0	99.00	1	Canceled
31648	2	0	2	0	Meal Plan 1	0	Room_Type 1	21	2019	6	11	Online	0	0	0	103.20	0	Not_Canceled
38401	1	0	2	3	Meal Plan 1	0	Room_Type 1	325	2018	11	6	Online	0	0	0	189.55	1	Canceled
37252	2	0	1	3	Meal Plan 1	0	Room_Type 1	75	2018	12	5	Offline	0	0	0	75.00	0	Not_Canceled
47697	2	0	1	3	Meal Plan 1	0	Room_Type 4	79	2018	4	4	Online	0	0	0	95.20	1	Not_Canceled
52088	3	0	2	4	Meal Plan 1	0	Room_Type 4	30	2018	7	7	Online	0	0	0	168.30	4	Not_Canceled
2675	2	2	0	3	Meal Plan 1	0	Room_Type 6	141	2019	5	25	Online	0	0	0	198.00	0	Not_Canceled
17381	2	0	1	2	Meal Plan 1	0	Room_Type 1	193	2018	6	20	Offline	0	0	0	120.00	0	Canceled
41904	2	0	1	3	Meal Plan 1	0	Room_Type 4	15	2018	12	5	Online	1	0	2	96.90	0	Not_Canceled
33494	2	0	0	1	Meal Plan 1	0	Room_Type 1	3	2018	2	27	Online	0	0	0	93.00	0	Not_Canceled
209	2	0	1	3	Meal Plan 1	0	Room_Type 1	123	2019	4	24	Online	0	0	0	123.75	1	Canceled
53636	2	0	0	1	Meal Plan 2	0	Room_Type 1	48	2017	9	11	Offline	0	0	0	104.00	0	Not_Canceled
45436	2	0	0	1	Meal Plan 1	0	Room_Type 1	3	2018	3	11	Online	0	0	0	109.00	1	Not_Canceled
20654	2	0	0	1	Meal Plan 1	0	Room_Type 1	1	2018	4	8	Online	0	0	0	101.00	1	Not_Canceled
11528	2	0	1	2	Meal Plan 1	0	Room_Type 4	40	2018	10	14	Online	0	0	0	145.20	2	Not_Canceled
33322	1	1	2	8	Meal Plan 1	0	Room_Type 4	162	2019	7	26	Offline	0	0	0	96.91	0	Canceled
18757	2	0	1	1	Meal Plan 1	0	Room_Type 1	92	2019	6	10	Online	0	0	0	115.20	0	Not_Canceled
2742	2	0	0	4	Meal Plan 1	0	Room_Type 1	28	2017	9	29	Online	0	0	0	96.36	1	Not_Canceled
56803	2	0	0	4	Meal Plan 1	0	Room_Type 4	147	2019	5	23	Online	0	0	0	139.50	1	Canceled
16925	2	0	0	3	Meal Plan 1	0	Room_Type 1	28	2018	10	20	Offline	0	0	0	85.00	0	Not_Canceled
3791	2	0	2	2	Not Selected	0	Room_Type 1	200	2018	8	6	Online	0	0	0	62.18	1	Not_Canceled
28358	2	0	2	3	Meal Plan 1	0	Room_Type 1	40	2018	6	26	Online	0	0	0	110.70	0	Not_Canceled
7746	2	0	1	2	Not Selected	0	Room_Type 1	42	2019	6	16	Online	0	0	0	140.00	0	Not_Canceled
11688	1	0	0	3	Meal Plan 1	0	Room_Type 1	58	2019	3	30	Online	0	0	0	94.80	0	Canceled
31058	2	0	0	3	Not Selected	0	Room_Type 1	17	2018	12	8	Online	0	0	0	92.67	0	Not_Canceled
15822	2	0	1	2	Meal Plan 1	0	Room_Type 4	57	2018	3	21	Online	0	0	0	118.50	1	Not_Canceled
12677	2	0	1	3	Meal Plan 1	0	Room_Type 1	39	2018	11	7	Online	0	0	0	147.60	0	Canceled
40621	0	2	1	4	Meal Plan 1	0	Room_Type 2	32	2018	9	12	Online	0	0	0	124.25	0	Canceled
32931	2	0	1	1	Meal Plan 1	0	Room_Type 1	0	2018	8	22	Corporate	0	0	0	100.00	0	Not_Canceled
49014	2	0	0	3	Meal Plan 1	0	Room_Type 1	37	2018	10	13	Offline	0	0	0	105.00	0	Not_Canceled
21260	3	0	2	5	Meal Plan 1	0	Room_Type 4	82	2019	6	4	Online	0	0	0	189.00	0	Not_Canceled
16352	2	0	2	2	Meal Plan 1	0	Room_Type 1	241	2019	3	31	Online	0	0	0	103.60	0	Canceled
33796	2	0	1	2	Meal Plan 1	0	Room_Type 1	146	2019	4	10	Online	0	0	0	121.33	2	Canceled
13401	2	0	0	1	Meal Plan 2	0	Room_Type 1	55	2018	4	6	Offline	0	0	0	104.00	0	Not_Canceled
23842	2	0	2	1	Meal Plan 1	0	Room_Type 1	116	2019	5	7	Offline	0	0	0	89.10	0	Not_Canceled
30429	2	0	0	1	Meal Plan 1	0	Room_Type 1	154	2019	3	1	Online	0	0	0	93.60	2	Not_Canceled
11596	2	2	0	1	Meal Plan 1	1	Room_Type 6	24	2017	10	24	Online	0	0	0	224.00	3	Not_Canceled
3143	2	0	2	1	Meal Plan 1	0	Room_Type 4	13	2018	9	4	Online	0	0	0	160.00	1	Not_Canceled
54319	2	0	0	2	Meal Plan 2	0	Room_Type 1	63	2017	9	4	Offline	0	0	0	116.00	0	Not_Canceled
36416	3	0	1	2	Meal Plan 1	0	Room_Type 4	186	2018	8	8	Online	0	0	0	137.70	2	Canceled
27194	2	0	0	3	Meal Plan 1	0	Room_Type 1	28	2017	9	29	Online	0	0	0	133.17	0	Not_Canceled
17259	2	0	1	0	Meal Plan 1	0	Room_Type 4	5	2018	5	1	Online	0	0	0	155.00	1	Not_Canceled
27501	2	0	0	3	Meal Plan 1	0	Room_Type 1	304	2018	11	3	Offline	0	0	0	89.00	0	Canceled
22930	2	0	0	2	Meal Plan 1	0	Room_Type 1	36	2017	11	13	Online	0	0	0	96.30	1	Not_Canceled
55598	3	0	1	2	Meal Plan 1	0	Room_Type 4	48	2019	4	7	Online	0	0	0	180.00	1	Canceled
51277	2	0	1	2	Meal Plan 1	0	Room_Type 1	66	2017	10	9	Offline	0	0	0	75.00	0	Canceled
551	2	0	2	1	Meal Plan 2	0	Room_Type 1	205	2019	4	15	Online	0	0	0	151.20	1	Canceled
11742	2	0	0	2	Meal Plan 1	0	Room_Type 1	315	2018	12	2	Offline	0	0	0	52.00	0	Not_Canceled
19900	2	0	0	4	Not Selected	0	Room_Type 1	0	2018	11	16	Online	0	0	0	59.94	0	Not_Canceled
7599	1	0	3	6	Meal Plan 1	0	Room_Type 1	34	2019	1	14	Online	0	0	0	62.83	1	Not_Canceled
23402	2	0	0	1	Meal Plan 1	0	Room_Type 1	27	2018	6	9	Offline	0	0	0	120.00	0	Not_Canceled
54071	2	0	2	3	Meal Plan 1	0	Room_Type 1	8	2018	10	30	Online	0	0	0	127.40	1	Not_Canceled
45867	1	0	1	1	Meal Plan 1	0	Room_Type 1	150	2019	2	13	Online	0	0	0	86.40	1	Not_Canceled
696	2	0	1	2	Not Selected	0	Room_Type 1	33	2019	5	22	Online	0	0	0	160.00	1	Not_Canceled
17301	3	0	0	5	Meal Plan 1	0	Room_Type 5	28	2019	2	21	Online	0	0	0	123.08	2	Not_Canceled
50954	2	0	2	1	Not Selected	0	Room_Type 1	15	2018	3	5	Online	0	0	0	84.33	0	Not_Canceled
36039	2	1	1	3	Meal Plan 1	0	Room_Type 1	52	2018	8	18	Online	0	0	0	152.10	3	Not_Canceled
45622	2	0	2	2	Meal Plan 1	0	Room_Type 1	28	2018	2	5	Online	0	0	0	84.65	0	Not_Canceled
37278	0	1	2	5	Meal Plan 1	0	Room_Type 2	239	2019	6	4	Online	0	0	0	107.45	1	Canceled
4220	1	0	1	2	Meal Plan 1	0	Room_Type 1	39	2018	3	11	Online	0	0	0	76.50	0	Canceled
23230	2	0	0	2	Not Selected	0	Room_Type 1	21	2018	2	12	Online	0	0	0	79.00	0	Not_Canceled
38741	2	0	1	1	Not Selected	0	Room_Type 1	34	2019	4	15	Online	0	0	0	120.00	1	Not_Canceled
35999	2	0	1	3	Meal Plan 1	0	Room_Type 4	30	2019	5	1	Online	0	0	0	179.00	0	Not_Canceled
13695	2	0	0	1	Meal Plan 1	0	Room_Type 1	56	2018	6	8	Offline	0	0	0	120.00	0	Not_Canceled
7408	1	0	2	3	Not Selected	0	Room_Type 1	224	2019	7	2	Online	0	0	0	89.10	0	Canceled
33285	2	0	1	2	Meal Plan 1	0	Room_Type 4	50	2018	9	2	Online	0	0	0	123.30	1	Canceled
3616	2	0	2	2	Not Selected	0	Room_Type 1	182	2019	4	23	Online	0	0	0	99.00	2	Canceled
37829	2	0	0	2	Meal Plan 1	0	Room_Type 1	54	2018	2	26	Online	0	0	0	80.30	0	Not_Canceled
19380	2	0	0	1	Meal Plan 1	0	Room_Type 2	4	2017	12	19	Online	0	0	0	77.25	1	Not_Canceled
6776	2	0	1	1	Not Selected	0	Room_Type 1	37	2018	12	10	Online	0	0	0	88.00	0	Canceled
9401	2	0	2	1	Meal Plan 2	0	Room_Type 1	150	2018	1	2	Offline	0	0	0	101.00	0	Not_Canceled
5172	2	0	1	1	Meal Plan 1	0	Room_Type 2	0	2017	12	5	Online	0	0	0	73.64	0	Not_Canceled
896	2	0	2	5	Not Selected	0	Room_Type 1	207	2019	7	12	Online	0	0	0	89.10	1	Not_Canceled
46710	1	0	1	0	Meal Plan 1	0	Room_Type 1	4	2019	2	13	Offline	0	0	0	75.00	0	Not_Canceled
29961	1	0	0	1	Meal Plan 1	0	Room_Type 1	1	2018	9	6	Corporate	0	0	0	95.00	0	Not_Canceled
53285	2	0	0	1	Meal Plan 1	0	Room_Type 1	37	2019	2	14	Online	0	0	0	98.00	1	Canceled
6842	1	0	0	2	Meal Plan 1	0	Room_Type 1	183	2019	6	8	Online	0	0	0	120.00	0	Canceled
35643	2	1	1	5	Meal Plan 1	0	Room_Type 1	122	2018	5	30	Online	0	0	0	119.85	3	Not_Canceled
48912	2	0	1	2	Meal Plan 1	0	Room_Type 5	82	2019	5	5	Online	0	0	0	162.00	2	Not_Canceled
28861	2	0	0	3	Meal Plan 1	0	Room_Type 1	148	2018	11	2	Online	0	0	0	65.83	1	Not_Canceled
31033	2	0	0	5	Not Selected	0	Room_Type 1	3	2019	8	8	Online	0	0	0	124.63	1	Not_Canceled
29492	2	0	2	1	Meal Plan 1	0	Room_Type 1	13	2018	5	8	Online	0	0	0	111.00	2	Not_Canceled
43886	2	0	1	3	Not Selected	0	Room_Type 1	79	2018	11	17	Online	0	0	0	74.80	1	Not_Canceled
43949	1	0	0	1	Meal Plan 1	0	Room_Type 1	15	2019	5	23	Online	0	0	0	140.00	1	Not_Canceled
42228	2	0	2	0	Meal Plan 1	0	Room_Type 2	243	2018	6	19	Online	0	0	0	95.58	1	Not_Canceled
8263	2	0	0	4	Meal Plan 1	0	Room_Type 1	168	2019	1	24	Online	0	0	0	85.00	1	Canceled
52055	2	0	1	4	Not Selected	0	Room_Type 1	113	2018	5	18	Online	0	0	0	126.65	0	Canceled
23476	2	0	1	2	Meal Plan 1	0	Room_Type 4	100	2019	8	14	Online	0	0	0	155.00	1	Canceled
39378	2	0	0	2	Meal Plan 2	0	Room_Type 1	291	2018	8	19	Offline	0	0	0	115.00	0	Canceled
4711	2	0	1	3	Meal Plan 1	0	Room_Type 4	7	2019	7	17	Online	0	0	0	170.00	1	Not_Canceled
6785	2	0	0	2	Meal Plan 1	0	Room_Type 1	124	2018	4	29	Online	0	0	0	96.30	0	Canceled
52511	1	0	0	1	Meal Plan 1	0	Room_Type 4	3	2018	11	10	Aviation	0	0	0	95.00	0	Canceled
26068	1	0	1	4	Meal Plan 1	0	Room_Type 1	191	2018	7	25	Online	0	0	0	86.75	0	Canceled
21992	2	0	1	3	Meal Plan 1	0	Room_Type 1	57	2019	4	3	Online	0	0	0	117.00	0	Canceled
23253	1	0	2	2	Meal Plan 1	0	Room_Type 1	37	2018	3	11	Online	0	0	0	76.50	0	Canceled
19476	2	0	0	1	Meal Plan 1	0	Room_Type 1	79	2017	9	18	Online	0	0	0	89.25	0	Canceled
34293	2	0	1	2	Meal Plan 1	1	Room_Type 4	43	2018	5	9	Online	0	0	0	149.40	1	Not_Canceled
11204	2	0	1	5	Meal Plan 1	0	Room_Type 4	64	2018	9	6	Online	0	0	0	136.80	0	Canceled

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
56916	3	0	0	3	Meal Plan 1	Room_Type 1	180	2019	5	25	Online	166.00	1	Canceled
56917	3	0	2	6	Meal Plan 1	Room_Type 4	85	2018	8	3	Online	167.80	1	Not_Canceled
56918	2	0	1	3	Meal Plan 1	Room_Type 1	228	2018	10	17	Online	90.95	2	Canceled
56919	2	0	2	1	Meal Plan 1	Room_Type 1	319	2019	6	24	Offline	90.00	0	Canceled
56920	2	0	2	6	Meal Plan 1	Room_Type 1	148	2018	7	1	Online	98.39	2	Not_Canceled
56921	2	1	0	1	Meal Plan 2	Room_Type 4	45	2019	6	15	Online	163.88	1	Not_Canceled
56922	2	0	1	1	Meal Plan 1	Room_Type 1	320	2019	5	15	Offline	90.00	1	Canceled
56923	2	0	0	3	Not Selected	Room_Type 1	63	2018	4	21	Online	94.50	0	Canceled
56924	2	0	2	2	Not Selected	Room_Type 1	6	2019	4	28	Online	162.50	2	Not_Canceled
56925	2	0	1	2	Meal Plan 1	Room_Type 1	207	2018	12	30	Offline	161.67	0	Not_Canceled

	count	mean	std	min	25%	50%	75%	max
no_of_adults	42576.0	1.916737	0.527524	0.0	2.0	2.0	2.0	4.0
no_of_children	42576.0	0.142146	0.459920	0.0	0.0	0.0	0.0	10.0
no_of_weekend_nights	42576.0	0.895270	0.887864	0.0	0.0	1.0	2.0	8.0
no_of_week_nights	42576.0	2.321167	1.519328	0.0	1.0	2.0	3.0	17.0
required_car_parking_space	42576.0	0.034362	0.182160	0.0	0.0	0.0	0.0	1.0
lead_time	42576.0	77.315953	77.279616	0.0	16.0	53.0	118.0	521.0
arrival_year	42576.0	2018.297891	0.626126	2017.0	2018.0	2018.0	2019.0	2019.0
arrival_month	42576.0	6.365488	3.051924	1.0	4.0	6.0	9.0	12.0
arrival_date	42576.0	15.682873	8.813991	1.0	8.0	16.0	23.0	31.0
repeated_guest	42576.0	0.030886	0.173011	0.0	0.0	0.0	0.0	1.0
no_of_previous_cancellations	42576.0	0.025413	0.358194	0.0	0.0	0.0	0.0	13.0
no_of_previous_bookings_not_canceled	42576.0	0.222731	2.242308	0.0	0.0	0.0	0.0	72.0
avg_price_per_room	42576.0	112.375800	40.865896	0.0	85.5	107.0	135.0	540.0
no_of_special_requests	42576.0	0.768109	0.837264	0.0	0.0	1.0	1.0	5.0

	type_of_meal_plan	room_type_reserved	market_segment_type	booking_status
count	42576	42576	42576	42576
unique	4	7	5	2
top	Meal Plan 1	Room_Type 1	Online	Not_Canceled
freq	31863	29730	34169	28089

	count	unique	top	freq
type_of_meal_plan	42576	4	Meal Plan 1	31863
room_type_reserved	42576	7	Room_Type 1	29730
market_segment_type	42576	5	Online	34169
booking_status	42576	2	Not_Canceled	28089

				arrival_year	month_in_chars	day_of_week	booking_status
arrival_year	month_in_chars	day_of_week	booking_status
2017	Apr	Friday	Canceled	0	0	0	0
		Friday	Not_Canceled	0	0	0	0
		Monday	Canceled	0	0	0	0
		Monday	Not_Canceled	0	0	0	0
		Saturday	Canceled	0	0	0	0
		Saturday	Not_Canceled	0	0	0	0
		Sunday	Canceled	0	0	0	0
		Sunday	Not_Canceled	0	0	0	0
		Thursday	Canceled	0	0	0	0
		Thursday	Not_Canceled	0	0	0	0
		Tuesday	Canceled	0	0	0	0
		Tuesday	Not_Canceled	0	0	0	0
		Wednesday	Canceled	0	0	0	0
		Wednesday	Not_Canceled	0	0	0	0
	Aug	Friday	Canceled	17	17	17	17
		Friday	Not_Canceled	76	76	76	76
		Monday	Canceled	14	14	14	14
		Monday	Not_Canceled	76	76	76	76
		Saturday	Canceled	10	10	10	10
		Saturday	Not_Canceled	83	83	83	83

	type_of_meal_plan	room_type_reserved	lead_time_log_plus_1	market_segment_type	avg_price_bin	day_of_week	booking_status	total_no_of_days	weekend_only_booking	total_no_of_person	adults_only_booking
0	Meal Plan 1	Room_Type 1	5.416100	Offline	50-100	Monday	0	3	0	2	1
1	Not Selected	Room_Type 1	1.791759	Online	100-200	Tuesday	0	5	0	2	1
2	Meal Plan 1	Room_Type 1	0.693147	Online	50-100	Wednesday	1	3	0	1	1
3	Meal Plan 1	Room_Type 1	5.356586	Online	50-100	Sunday	1	2	0	2	1
4	Not Selected	Room_Type 1	5.627621	Online	50-100	Saturday	1	3	0	3	1
5	Not Selected	Room_Type 1	3.891820	Online	50-100	Wednesday	1	2	0	2	1
6	Meal Plan 1	Room_Type 1	3.663562	Online	100-200	Thursday	0	3	0	1	1
7	Meal Plan 2	Room_Type 1	5.849325	Online	100-200	Thursday	1	2	0	2	1
8	Meal Plan 1	Room_Type 1	3.555348	Online	100-200	Sunday	0	4	0	2	1
9	Meal Plan 1	Room_Type 1	4.897840	Offline	100-200	Friday	0	4	0	2	1
10	Meal Plan 1	Room_Type 4	4.430817	Online	100-200	Wednesday	0	4	0	2	1
11	Meal Plan 1	Room_Type 1	4.804021	Offline	50-100	Friday	0	4	0	3	1
12	Meal Plan 1	Room_Type 4	3.806662	Online	100-200	Thursday	0	5	0	2	1
13	Not Selected	Room_Type 1	0.000000	Online	50-100	Tuesday	0	1	1	1	1
14	Meal Plan 1	Room_Type 4	3.583519	Online	100-200	Monday	0	3	0	1	1
15	Meal Plan 1	Room_Type 1	3.465736	Online	100-200	Tuesday	0	1	1	2	1
16	Not Selected	Room_Type 1	4.488636	Online	100-200	Friday	1	3	0	2	1
17	Meal Plan 1	Room_Type 1	5.393628	Online	50-100	Sunday	0	7	0	1	1
18	Meal Plan 1	Room_Type 1	4.248495	Corporate	50-100	Saturday	1	3	0	1	1
19	Not Selected	Room_Type 1	3.433987	Online	50-100	Monday	1	3	0	2	1

	feature	VIF
0	const	345.011466
1	required_car_parking_space	1.029513
2	lead_time_log_plus_1	1.270061
3	repeated_guest	2.013035
4	no_of_previous_cancellations	1.507578
5	no_of_previous_bookings_not_canceled	1.837324
6	total_no_of_days	1.234856
7	weekend_only_booking	1.300290
8	total_no_of_person	2.338811
9	adults_only_booking	1.968416
10	type_of_meal_plan_Meal Plan 2	1.072586
11	type_of_meal_plan_Meal Plan 3	1.028442
12	type_of_meal_plan_Not Selected	1.269615
13	room_type_reserved_Room_Type 2	1.053128
14	room_type_reserved_Room_Type 3	1.001075
15	room_type_reserved_Room_Type 4	1.407238
16	room_type_reserved_Room_Type 5	1.102537
17	room_type_reserved_Room_Type 6	1.731621
18	room_type_reserved_Room_Type 7	1.261948
19	market_segment_type_Complementary	4.660871
20	market_segment_type_Corporate	10.747089
21	market_segment_type_Offline	27.569244
22	market_segment_type_Online	36.523389
23	avg_price_bin_50-100	20.575404
24	avg_price_bin_100-200	21.396675
25	avg_price_bin_200-250	3.464228
26	avg_price_bin_250-300	1.486178
27	avg_price_bin_Above_300	1.073410
28	day_of_week_Monday	1.801223
29	day_of_week_Saturday	1.827173
30	day_of_week_Sunday	1.881623
31	day_of_week_Thursday	1.742216
32	day_of_week_Tuesday	2.003141
33	day_of_week_Wednesday	1.967229

	feature	VIF
0	const	63.633343
1	required_car_parking_space	1.025967
2	lead_time_log_plus_1	1.238281
3	repeated_guest	1.671563
4	no_of_previous_cancellations	1.487011
5	no_of_previous_bookings_not_canceled	1.811189
6	total_no_of_days	1.226644
7	weekend_only_booking	1.299263
8	total_no_of_person	2.241506
9	adults_only_booking	1.956427
10	type_of_meal_plan_Meal Plan 2	1.071195
11	type_of_meal_plan_Meal Plan 3	1.028434
12	type_of_meal_plan_Not Selected	1.232006
13	room_type_reserved_Room_Type 2	1.051074
14	room_type_reserved_Room_Type 3	1.001065
15	room_type_reserved_Room_Type 4	1.400628
16	room_type_reserved_Room_Type 5	1.096784
17	room_type_reserved_Room_Type 6	1.727726
18	room_type_reserved_Room_Type 7	1.260770
19	market_segment_type_Complementary	1.133893
20	market_segment_type_Offline	1.187911
21	avg_price_bin_100-200	1.436691
22	avg_price_bin_200-250	1.468578
23	avg_price_bin_250-300	1.170892
24	avg_price_bin_Above_300	1.029266
25	day_of_week_Monday	1.798128
26	day_of_week_Saturday	1.826049
27	day_of_week_Sunday	1.878803
28	day_of_week_Thursday	1.741809
29	day_of_week_Tuesday	1.997811
30	day_of_week_Wednesday	1.966597

Star Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Read the dataset¶

Check for any missing data¶

Check the info of the data¶

Check for duplicates¶

Let's drop the duplicate values¶

Lets check the different unique values for the object columns¶

Change the data type to be categorical¶

EDA¶

Functions for plotting¶

Univariate Analysis¶

arrival_month¶

arrival_year¶

arrival_date¶

type_of_meal_plan¶

room_type_reserved¶

market_segment_type¶

booking_status¶

Add required_car_parking_space and repeated_guest columns to category type¶

required_car_parking_space¶

repeated_guest¶

'no_of_adults' & 'no_of_children'¶

'no_of_weekend_nights' & 'no_of_week_nights'¶

EDA for Numeric columns¶

lead_time¶

avg_price_per_room¶

no_of_special_requests¶

Bivariate Analysis¶

'no_of_week_nights' Vs 'booking_status'¶

'no_of_adults' Vs 'booking_status'¶

'no_of_children','booking_status'¶

'no_of_weekend_nights' Vs 'booking_status¶

'no_of_week_nights' Vs 'booking_status'¶

'type_of_meal_plan' Vs 'booking_status'¶

'market_segment_type' Vs 'booking_status'¶

'repeated_guest' Vs 'booking_status'¶

'no_of_special_requests' Vs 'booking_status'¶

'no_of_previous_cancellations' Vs 'booking_status'¶

'lead_time' Vs 'booking_status'¶

'avg_price_per_room' Vs 'booking_status'¶

'avg_price_per_room' Vs 'room_type_reserved' Vs 'booking_status'¶

'avg_price_per_room' Vs 'type_of_meal_plan' Vs 'booking_status'¶

'avg_price_per_room' Vs 'market_segment_type'¶

Data Overview¶

Data Preprocessing¶

Outlier detection¶

lead_time - Outlier treatment¶

avg_price_per_room - Outlier treatment¶

Binning - avg_price_per_room¶

Create bin for the used price¶

Preparing data for modeling¶

Feature engineering¶

Add Day of the week¶

Add total_no_of_person¶

Add total_no_of_days¶

Change day_of_week to be category¶

Let's take the relevant columns for the model¶

Model evaluation criterion¶

Model can make wrong predictions as:¶

Which Loss is greater ?¶

How to reduce this loss i.e need to reduce False Negatives ?¶

Positive event and Negative Event¶

Data Preparation¶

Checking Multicollinearity¶

Building a Logistic Regression model¶

Logistic Regression (with Sklearn library)¶

Checking performance on training set¶

Checking performance on test set¶

Logistic Regression (with statsmodels library)¶

logit sm model¶

Remove the hign VIF columns¶

None of the columns have high VIF¶

Converting coefficients to odds¶

Coefficient interpretations¶

Checking model performance on the training set¶

ROC-AUC¶